Microbial strain improvement by a HTP genomic engineering platform

ABSTRACT

The present disclosure provides a HTP microbial genomic engineering platform that is computationally driven and integrates molecular biology, automation, and advanced machine learning protocols. This integrative platform utilizes a suite of HTP molecular tool sets to create HTP genetic design libraries, which are derived from, inter alia, scientific insight and iterative pattern recognition. The HTP genomic engineering platform described herein is microbial strain host agnostic and therefore can be implemented across taxa. Furthermore, the disclosed platform can be implemented to modulate or improve any microbial host parameter of interest.

CROSS-REFERENCE TO RELATED APPLICATIONS

This Application is a Continuation Application of U.S. application Ser.No. 16/458,376, filed on Jul. 1, 2019, now issued as U.S. Pat. No.10,647,980, which is itself a Continuation of U.S. application Ser. No.15/923,527, filed on Mar. 16, 2018, now issued as U.S. Pat. No.10,336,998, which is itself a Continuation of U.S. application Ser. No.15/396,230, filed on Dec. 30, 2016, now issued as U.S. Pat. No.9,988,624, which is itself a Continuation of U.S. Utility Applicationunder 35 U.S.C. § 111, claiming the benefit of priority to InternationalApplication No. PCT/US2016/065465, filed on Dec. 7, 2016, which claimsthe benefit of priority to U.S. Provisional Application No. 62/264,232,filed on Dec. 7, 2015, U.S. Nonprovisional application Ser. No.15/140,296, filed on Apr. 27, 2016, and U.S. Provisional Application No.62/368,786, filed on Jul. 29, 2016, each of which are herebyincorporated by reference in their entirety, including all descriptions,references, figures, and claims for all purposes.

FIELD

The present disclosure is directed to high-throughput (HTP) microbialgenomic engineering. The disclosed HTP genomic engineering platform iscomputationally driven and integrates molecular biology, automation, andadvanced machine learning protocols. This integrative platform utilizesa suite of HTP molecular tool sets to create HTP genetic designlibraries, which are derived from, inter alia, scientific insight anditerative pattern recognition.

STATEMENT REGARDING SEQUENCE LISTING

The Sequence Listing associated with this application is provided intext format in lieu of a paper copy, and is hereby incorporated byreference into the specification. The name of the text file containingthe Sequence Listing is ZYMR_001_02US_SeqList_ST25.txt. The text file is5 KB, was created on Feb. 23, 2018, and is being submittedelectronically via EFS-Web.

BACKGROUND

Humans have been harnessing the power of microbial cellular biosyntheticpathways for millennia to produce products of interest, the oldestexamples of which include alcohol, vinegar, cheese, and yogurt. Theseproducts are still in large demand today and have also been accompaniedby an ever increasing repertoire of products producible by microbes. Theadvent of genetic engineering technology has enabled scientists todesign and program novel biosynthetic pathways into a variety oforganisms to produce a broad range of industrial, medical, and consumerproducts. Indeed, microbial cellular cultures are now used to produceproducts ranging from small molecules, antibiotics, vaccines,insecticides, enzymes, fuels, and industrial chemicals.

Given the large number of products produced by modern industrialmicrobes, it comes as no surprise that engineers are under tremendouspressure to improve the speed and efficiency by which a givenmicroorganism is able to produce a target product.

A variety of approaches have been used to improve the economy ofbiologically-based industrial processes by “improving” the microorganisminvolved. For example, many pharmaceutical and chemical industries relyon microbial strain improvement programs in which the parent strains ofa microbial culture are continuously mutated through exposure tochemicals or UV radiation and are subsequently screened for performanceincreases, such as in productivity, yield and titer. This mutagenesisprocess is extensively repeated until a strain demonstrates a suitableincrease in product performance. The subsequent “improved” strain isthen utilized in commercial production.

As alluded to above, identification of improved industrial microbialstrains through mutagenesis is time consuming and inefficient. Theprocess, by its very nature, is haphazard and relies upon one stumblingupon a mutation that has a desirable outcome on product output.

Not only are traditional microbial strain improvement programsinefficient, but the process can also lead to industrial strains with ahigh degree of detrimental mutagenic load. The accumulation of mutationsin industrial strains subjected to these types of programs can becomesignificant and may lead to an eventual stagnation in the rate ofperformance improvement.

Thus, there is a great need in the art for new methods of engineeringindustrial microbes, which do not suffer from the aforementioneddrawbacks inherent with traditional strain improvement programs andgreatly accelerate the process of discovering and consolidatingbeneficial mutations.

Further, there is an urgent need for a method by which to “rehabilitate”industrial strains that have been developed by the antiquated anddeleterious processes currently employed in the field of microbialstrain improvement.

SUMMARY OF THE DISCLOSURE

The present disclosure provides a high-throughput (HTP) microbialgenomic engineering platform that does not suffer from the myriad ofproblems associated with traditional microbial strain improvementprograms.

Further, the HTP platform taught herein is able to rehabilitateindustrial microbes that have accumulated non-beneficial mutationsthrough decades of random mutagenesis-based strain improvement programs.

The disclosed HTP genomic engineering platform is computationally drivenand integrates molecular biology, automation, and advanced machinelearning protocols. This integrative platform utilizes a suite of HTPmolecular tool sets to create HTP genetic design libraries, which arederived from, inter alia, scientific insight and iterative patternrecognition.

The taught HTP genetic design libraries function as drivers of thegenomic engineering process, by providing libraries of particulargenomic alterations for testing in a microbe. The microbes engineeredutilizing a particular library, or combination of libraries, areefficiently screened in a HTP manner for a resultant outcome, e.g.production of a product of interest. This process of utilizing the HTPgenetic design libraries to define particular genomic alterations fortesting in a microbe and then subsequently screening host microbialgenomes harboring the alterations is implemented in an efficient anditerative manner. In some aspects, the iterative cycle or “rounds” ofgenomic engineering campaigns can be at least 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or moreiterations/cycles/rounds.

Thus, in some aspects, the present disclosure teaches methods ofconducting at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 50,51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86,87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 125, 150, 175,200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 525,550, 575, 600, 625, 650, 675, 700, 725, 750, 775, 800, 825, 850, 875,900, 925, 950, 975, 1000 or more “rounds” of HTP genetic engineering(e.g., rounds of SNP swap, PRO swap, STOP swap, or combinationsthereof).

In some embodiments the present disclosure teaches a linear approach, inwhich each subsequent HTP genetic engineering round is based on geneticvariation identified in the previous round of genetic engineering. Inother embodiments the present disclosure teaches a non-linear approach,in which each subsequent HTP genetic engineering round is based ongenetic variation identified in any previous round of geneticengineering, including previously conducted analysis, and separate HTPgenetic engineering branches.

The data from these iterative cycles enables large scale data analyticsand pattern recognition, which is utilized by the integrative platformto inform subsequent rounds of HTP genetic design libraryimplementation. Consequently, the HTP genetic design libraries utilizedin the taught platform are highly dynamic tools that benefit from largescale data pattern recognition algorithms and become more informativethrough each iterative round of microbial engineering.

In some embodiments, the genetic design libraries of the presentdisclosure comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49,50, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 125,150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475,500, 525, 550, 575, 600, 625, 650, 675, 700, 725, 750, 775, 800, 825,850, 875, 900, 925, 950, 975, 1000 or more individual genetic changes(e.g., at least X number of promoter:gene combinations in the PRO swaplibrary).

In some embodiments, the present disclosure provides illustrativeexamples and text describing application of HTP strain improvementmethods to microbial strains. In some embodiments, the strainimprovement methods of the present disclosure are applicable to any hostcell.

In some embodiments, the present disclosure teaches a high-throughput(HTP) method of genomic engineering to evolve a microbe to acquire adesired phenotype, comprising: a) perturbing the genomes of an initialplurality of microbes having the same microbial strain background, tothereby create an initial HTP genetic design microbial strain librarycomprising individual microbial strains with unique genetic variations;b) screening and selecting individual microbial strains of the initialHTP genetic design microbial strain library for the desired phenotype;c) providing a subsequent plurality of microbes that each comprise aunique combination of genetic variation, said genetic variation selectedfrom the genetic variation present in at least two individual microbialstrains screened in the preceding step, to thereby create a subsequentHTP genetic design microbial strain library; d) screening and selectingindividual microbial strains of the subsequent HTP genetic designmicrobial strain library for the desired phenotype; e) repeating stepsc)-d) one or more times, in a linear or non-linear fashion, until amicrobe has acquired the desired phenotype, wherein each subsequentiteration creates a new HTP genetic design microbial strain librarycomprising individual microbial strains harboring unique geneticvariations that are a combination of genetic variation selected fromamongst at least two individual microbial strains of a preceding HTPgenetic design microbial strain library.

In some embodiments, the present disclosure teaches that the initial HTPgenetic design microbial strain library is at least one selected fromthe group consisting of a promoter swap microbial strain library, SNPswap microbial strain library, start/stop codon microbial strainlibrary, optimized sequence microbial strain library, a terminator swapmicrobial strain library, or any combination thereof.

In some embodiments, the present disclosure teaches methods of making asubsequent plurality of microbes that each comprise a unique combinationof genetic variations, wherein each of the combined genetic variationsis derived from the initial HTP genetic design microbial strain libraryor the HTP genetic design microbial strain library of the precedingstep.

In some embodiments, the combination of genetic variations in thesubsequent plurality of microbes will comprise a subset of all thepossible combinations of the genetic variations in the initial HTPgenetic design microbial strain library or the HTP genetic designmicrobial strain library of the preceding step.

In some embodiments, the present disclosure teaches that the subsequentHTP genetic design microbial strain library is a full combinatorialmicrobial strain library derived from the genetic variations in theinitial HTP genetic design microbial strain library or the HTP geneticdesign microbial strain library of the preceding step.

For example, if the prior HTP genetic design microbial strain libraryonly had genetic variations A, B, C, and D, then a partial combinatorialof said variations could include a subsequent HTP genetic designmicrobial strain library comprising three microbes each comprisingeither the AB, AC, or AD unique combinations of genetic variations(order in which the mutations are represented is unimportant). A fullcombinatorial microbial strain library derived from the geneticvariations of the HTP genetic design library of the preceding step wouldinclude six microbes, each comprising either AB, AC, AD, BC, BD, or CDunique combinations of genetic variations.

In some embodiments, the methods of the present disclosure teachperturbing the genome utilizing at least one method selected from thegroup consisting of: random mutagenesis, targeted sequence insertions,targeted sequence deletions, targeted sequence replacements, or anycombination thereof.

In some embodiments of the presently disclosed methods, the initialplurality of microbes comprise unique genetic variations derived from anindustrial production strain microbe.

In some embodiments of the presently disclosed methods, the initialplurality of microbes comprise industrial production strain microbesdenoted S₁Gen₁ and any number of subsequent microbial generationsderived therefrom denoted S_(n)Gen_(n).

In some embodiments, the present disclosure teaches a method forgenerating a SNP swap microbial strain library, comprising the steps of:a) providing a reference microbial strain and a second microbial strain,wherein the second microbial strain comprises a plurality of identifiedgenetic variations selected from single nucleotide polymorphisms, DNAinsertions, and DNA deletions, which are not present in the referencemicrobial strain; b) perturbing the genome of either the referencemicrobial strain, or the second microbial strain, to thereby create aninitial SNP swap microbial strain library comprising a plurality ofindividual microbial strains with unique genetic variations found withineach strain of said plurality of individual microbial strains, whereineach of said unique genetic variations corresponds to a single geneticvariation selected from the plurality of identified genetic variationsbetween the reference microbial strain and the second microbial strain.

In some embodiments of SNP swap library, the genome of the referencemicrobial strain is perturbed to add one or more of the identifiedsingle nucleotide polymorphisms, DNA insertions, or DNA deletions, whichare found in the second microbial strain.

In some embodiments of SNP swap library methods of the presentdisclosure, the genome of the second microbial strain is perturbed toremove one or more of the identified single nucleotide polymorphisms,DNA insertions, or DNA deletions, which are not found in the referencemicrobial strain.

In some embodiments, the genetic variations of the SNP swap library willcomprise a subset of all the genetic variations identified between thereference microbial strain and the second microbial strain.

In some embodiments, the genetic variations of the SNP swap library willcomprise all of the identified genetic variations identified between thereference microbial strain and the second microbial strain.

In some embodiments, the present disclosure teaches a method forrehabilitating and improving the phenotypic performance of an industrialmicrobial strain, comprising the steps of: a) providing a parentallineage microbial strain and an industrial microbial strain derivedtherefrom, wherein the industrial microbial strain comprises a pluralityof identified genetic variations selected from single nucleotidepolymorphisms, DNA insertions, and DNA deletions, not present in theparental lineage microbial strain; b) perturbing the genome of eitherthe parental lineage microbial strain, or the industrial microbialstrain, to thereby create an initial SNP swap microbial strain librarycomprising a plurality of individual microbial strains with uniquegenetic variations found within each strain of said plurality ofindividual microbial strains, wherein each of said unique geneticvariations corresponds to a single genetic variation selected from theplurality of identified genetic variations between the parental lineagemicrobial strain and the industrial microbial strain; c) screening andselecting individual microbial strains of the initial SNP swap microbialstrain library for phenotype performance improvements over a referencemicrobial strain, thereby identifying unique genetic variations thatconfer said microbial strains with phenotype performance improvements;d) providing a subsequent plurality of microbes that each comprise aunique combination of genetic variation, said genetic variation selectedfrom the genetic variation present in at least two individual microbialstrains screened in the preceding step, to thereby create a subsequentSNP swap microbial strain library; e) screening and selecting individualmicrobial strains of the subsequent SNP swap microbial strain libraryfor phenotype performance improvements over the reference microbialstrain, thereby identifying unique combinations of genetic variationthat confer said microbial strains with additional phenotype performanceimprovements; and f) repeating steps d)-e) one or more times, in alinear or non-linear fashion, until a microbial strain exhibits adesired level of improved phenotype performance compared to thephenotype performance of the industrial microbial strain, wherein eachsubsequent iteration creates a new SNP swap microbial strain librarycomprising individual microbial strains harboring unique geneticvariations that are a combination of genetic variation selected fromamongst at least two individual microbial strains of a preceding SNPswap microbial strain library.

In some embodiments the present disclosure teaches methods forrehabilitating and improving the phenotypic performance of an industrialmicrobial strain, wherein the genome of the parental lineage microbialstrain is perturbed to add one or more of the identified singlenucleotide polymorphisms, DNA insertions, or DNA deletions, which arefound in the industrial microbial strain.

In some embodiments the present disclosure teaches methods forrehabilitating and improving the phenotypic performance of an industrialmicrobial strain, wherein the genome of the industrial microbial strainis perturbed to remove one or more of the identified single nucleotidepolymorphisms, DNA insertions, or DNA deletions, which are not found inthe parental lineage microbial strain.

In some embodiments, the present disclosure teaches a method forgenerating a promoter swap microbial strain library, said methodcomprising the steps of: a) providing a plurality of target genesendogenous to a base microbial strain, and a promoter ladder, whereinsaid promoter ladder comprises a plurality of promoters exhibitingdifferent expression profiles in the base microbial strain; b)engineering the genome of the base microbial strain, to thereby createan initial promoter swap microbial strain library comprising a pluralityof individual microbial strains with unique genetic variations foundwithin each strain of said plurality of individual microbial strains,wherein each of said unique genetic variations comprises one of thepromoters from the promoter ladder operably linked to one of the targetgenes endogenous to the base microbial strain.

In some embodiments, the present disclosure teaches a promoter swapmethod of genomic engineering to evolve a microbe to acquire a desiredphenotype, said method comprising the steps of: a) providing a pluralityof target genes endogenous to a base microbial strain, and a promoterladder, wherein said promoter ladder comprises a plurality of promotersexhibiting different expression profiles in the base microbial strain;b) engineering the genome of the base microbial strain, to therebycreate an initial promoter swap microbial strain library comprising aplurality of individual microbial strains with unique genetic variationsfound within each strain of said plurality of individual microbialstrains, wherein each of said unique genetic variations comprises one ofthe promoters from the promoter ladder operably linked to one of thetarget genes endogenous to the base microbial strain; c) screening andselecting individual microbial strains of the initial promoter swapmicrobial strain library for the desired phenotype; d) providing asubsequent plurality of microbes that each comprise a unique combinationof genetic variation, said genetic variation selected from the geneticvariation present in at least two individual microbial strains screenedin the preceding step, to thereby create a subsequent promoter swapmicrobial strain library; e) screening and selecting individualmicrobial strains of the subsequent promoter swap microbial strainlibrary for the desired phenotype; f) repeating steps d)-e) one or moretimes, in a linear or non-linear fashion, until a microbe has acquiredthe desired phenotype, wherein each subsequent iteration creates a newpromoter swap microbial strain library comprising individual microbialstrains harboring unique genetic variations that are a combination ofgenetic variation selected from amongst at least two individualmicrobial strains of a preceding promoter swap microbial strain library.

In some embodiments, the present disclosure teaches a method forgenerating a terminator swap microbial strain library, said methodcomprising the steps of: a) providing a plurality of target genesendogenous to a base microbial strain, and a terminator ladder, whereinsaid terminator ladder comprises a plurality of terminators exhibitingdifferent expression profiles in the base microbial strain; b)engineering the genome of the base microbial strain, to thereby createan initial terminator swap microbial strain library comprising aplurality of individual microbial strains with unique genetic variationsfound within each strain of said plurality of individual microbialstrains, wherein each of said unique genetic variations comprises one ofthe target genes endogenous to the base microbial strain operably linkedto one or more of the terminators from the terminator ladder.

In some embodiments, the present disclosure teaches a terminator swapmethod of genomic engineering to evolve a microbe to acquire a desiredphenotype, said method comprising the steps of: a) providing a pluralityof target genes endogenous to a base microbial strain, and a terminatorladder, wherein said terminator ladder comprises a plurality ofterminators exhibiting different expression profiles in the basemicrobial strain; b) engineering the genome of the base microbialstrain, to thereby create an initial terminator swap microbial strainlibrary comprising a plurality of individual microbial strains withunique genetic variations found within each strain of said plurality ofindividual microbial strains, wherein each of said unique geneticvariations comprises one of the target genes endogenous to the basemicrobial strain operably linked to one or more of the terminators fromthe terminator ladder; c) screening and selecting individual microbialstrains of the initial terminator swap microbial strain library for thedesired phenotype; d) providing a subsequent plurality of microbes thateach comprise a unique combination of genetic variation, said geneticvariation selected from the genetic variation present in at least twoindividual microbial strains screened in the preceding step, to therebycreate a subsequent terminator swap microbial strain library; e)screening and selecting individual microbial strains of the subsequentterminator swap microbial strain library for the desired phenotype; f)repeating steps d)-e) one or more times, in a linear or non-linearfashion, until a microbe has acquired the desired phenotype, whereineach subsequent iteration creates a new terminator swap microbial strainlibrary comprising individual microbial strains harboring unique geneticvariations that are a combination of genetic variation selected fromamongst at least two individual microbial strains of a precedingterminator swap microbial strain library.

In some embodiments, the present disclosure teaches iterativelyimproving the design of candidate microbial strains by (a) accessing apredictive model populated with a training set comprising (1) inputsrepresenting genetic changes to one or more background microbial strainsand (2) corresponding performance measures; (b) applying test inputs tothe predictive model that represent genetic changes, the test inputscorresponding to candidate microbial strains incorporating those geneticchanges; (c) predicting phenotypic performance of the candidatemicrobial strains based at least in part upon the predictive model; (d)selecting a first subset of the candidate microbial strains based atleast in part upon their predicted performance; (e) obtaining measuredphenotypic performance of the first subset of the candidate microbialstrains; (f) obtaining a selection of a second subset of the candidatemicrobial strains based at least in part upon their measured phenotypicperformance; (g) adding to the training set of the predictive model (1)inputs corresponding to the selected second subset of candidatemicrobial strains, along with (2) corresponding measured performance ofthe selected second subset of candidate microbial strains; and (h)repeating (b)-(g) until measured phenotypic performance of at least onecandidate microbial strain satisfies a performance metric. In somecases, during a first application of test inputs to the predictivemodel, the genetic changes represented by the test inputs comprisegenetic changes to the one or more background microbial strains; andduring subsequent applications of test inputs, the genetic changesrepresented by the test inputs comprise genetic changes to candidatemicrobial strains within a previously selected second subset ofcandidate microbial strains.

In some embodiments, selection of the first subset may be based onepistatic effects. This may be achieved by: during a first selection ofthe first subset: determining degrees of dissimilarity betweenperformance measures of the one or more background microbial strains inresponse to application of a plurality of respective inputs representinggenetic changes to the one or more background microbial strains; andselecting for inclusion in the first subset at least two candidatemicrobial strains based at least in part upon the degrees ofdissimilarity in the performance measures of the one or more backgroundmicrobial strains in response to application of genetic changesincorporated into the at least two candidate microbial strains.

In some embodiments, the present invention teaches applying epistaticeffects in the iterative improvement of candidate microbial strains, themethod comprising: obtaining data representing measured performance inresponse to corresponding genetic changes made to at least one microbialbackground strain; obtaining a selection of at least two genetic changesbased at least in part upon a degree of dissimilarity between thecorresponding responsive performance measures of the at least twogenetic changes, wherein the degree of dissimilarity relates to thedegree to which the at least two genetic changes affect theircorresponding responsive performance measures through differentbiological pathways; and designing genetic changes to a microbialbackground strain that include the selected genetic changes. In somecases, the microbial background strain for which the at least twoselected genetic changes are designed is the same as the at least onemicrobial background strain for which data representing measuredresponsive performance was obtained.

In some embodiments, the present disclosure teaches HTP strainimprovement methods utilizing only a single type of genetic microbiallibrary. For example, in some embodiments, the present disclosureteaches HTP strain improvement methods utilizing only SNP swaplibraries. In other embodiments, the present disclosure teaches HTPstrain improvement methods utilizing only PRO swap libraries. In someembodiments, the present disclosure teaches HTP strain improvementmethods utilizing only STOP swap libraries. In some embodiments, thepresent disclosure teaches HTP strain improvement methods utilizing onlyStart/Stop Codon swap libraries.

In other embodiments, the present disclosure teaches HTP strainimprovement methods utilizing two or more types of genetic microbiallibraries. For example, in some embodiments, the present disclosureteaches HTP strain improvement methods combining SNP swap and PRO swaplibraries. In some embodiments, the present disclosure teaches HTPstrain improvement methods combining SNP swap and STOP swap libraries.In some embodiments, the present disclosure teaches HTP strainimprovement methods combining PRO swap and STOP swap libraries.

In other embodiments, the present disclosure teaches HTP strainimprovement methods utilizing multiple types of genetic microbiallibraries. In some embodiments the genetic microbial libraries arecombined to produce combination mutations (e.g., promoter/terminatorcombination ladders applied to one or more genes). In yet otherembodiments, the HTP strain improvement methods of the presentdisclosure can be combined with one or more traditional strainimprovement methods.

In some embodiments, the HTP strain improvement methods of the presentdisclosure result in an improved host cell. That is, the presentdisclosure teaches methods of improving one or more host cellproperties. In some embodiments the improved host cell property isselected from the group consisting of volumetric productivity, specificproductivity, yield or titre, of a product of interest produced by thehost cell. In some embodiments the improved host cell property isvolumetric productivity. In some embodiments the improved host cellproperty is specific productivity. In some embodiments the improved hostcell property is yield.

In some embodiments, the HTP strain improvement methods of the presentdisclosure in a host cell that exhibits a 1%, 2%, 3%, 4%, 5%, 6%, 7%,8%, 9%, 10%, 11%, 12%, result 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%,21%, 22%, 23% 24% 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%,36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%,50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%,64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%,78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%,92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 100%, 150%, 200% 250% 300% ormore of an improvement in at least one host cell property over a controlhost cell that is not subjected to the HTP strain improvements methods(e.g, an X % field improvement in y or productivity biomolecule ofinterest, incorporating any ranges and subranges therebetween). In someembodiments, the HTP strain improvement methods of the presentdisclosure are selected from the group consisting of SNP swap, PRO swap,STOP swap, and combinations thereof.

Thus, in some embodiments, the SNP swap methods of the presentdisclosure result in a host cell that exhibits a 1%, 2%, 3%, 4%, 5%, 6%,7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%,22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%,36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, %, 48%, 49%, 50%,51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%,65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73% 74% 75%, 76%, 77%, 78%, 79%,80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%,94%, 95% 9, 96%, 97%, 98%, 99%, 100%, 150%, 200%, 250%, 300% or more ofan improvement in a least one host cell property over a control hostcell that is not subjected to the SNP swap methods (e.g, an X %improvement in yield or productivity of a biomolecule of interest,incorporating any ranges and subranges therebetween).

Thus, in some embodiments, the PRO swap methods of the presentdisclosure result in a host cell that exhibits a 1%, 2%, 3%, 4%, 5%, 6%,7%, 8%, 9%, 10%, 11%, 12%, 13%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%,23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%,37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%,51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%,65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%,79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%,93%, 94%, 95%, 96%, 97%, 98%, 99%, 100%, 150%, 200%, 250%, 300% or moreof an improvement in at least one host cell property over a control hostcell that is not subjected to the PRO swap methods (e.g, an X %improvement in yield or productivity of a biomolecule of interest,incorporating any ranges and subranges therebetween).

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 depicts a DNA recombination method of the present disclosure forincreasing variation in diversity pools. DNA sections, such as genomeregions from related species, can be cut via physical orenzymatic/chemical means. The cut DNA regions are melted and allowed toreanneal, such that overlapping genetic regions prime polymeraseextension reactions. Subsequent melting/extension reactions are carriedout until products are reassembled into chimeric DNA, comprisingelements from one or more starting sequences.

FIG. 2 outlines methods of the present disclosure for generating newhost organisms with selected sequence modifications (e.g., 100 SNPs toswap). Briefly, the method comprises (1) desired DNA inserts aredesigned and generated by combining one or more synthesized oligos in anassembly reaction, (2) DNA inserts are cloned into transformationplasmids, (3) completed plasmids are transferred into desired productionstrains, where they are integrated into the host strain genome, and (4)selection markers and other unwanted DNA elements are looped out of thehost strain. Each DNA assembly step may involve additional qualitycontrol (QC) steps, such as cloning plasmids into E. coli bacteria foramplification and sequencing.

FIG. 3 depicts assembly of transformation plasmids of the presentdisclosure, and their integration into host organisms. The insert DNA isgenerated by combining one or more synthesized oligos in an assemblyreaction. DNA inserts containing the desired sequence are flanked byregions of DNA homologous to the targeted region of the genome. Thesehomologous regions facilitate genomic integration, and, once integrated,form direct repeat regions designed for looping out vector backbone DNAin subsequent steps. Assembled plasmids contain the insert DNA, andoptionally, one or more selection markers.

FIG. 4 depicts procedure for looping-out selected regions of DNA fromhost strains. Direct repeat regions of the inserted DNA and host genomecan “loop out” in a recombination event. Cells counter selected for theselection marker contain deletions of the loop DNA flanked by the directrepeat regions.

FIG. 5 depicts an embodiment of the strain improvement process of thepresent disclosure. Host strain sequences containing geneticmodifications (Genetic Design) are tested for strain performanceimprovements in various strain backgrounds (Strain Build). Strainsexhibiting beneficial mutations are analyzed (Hit ID and Analysis) andthe data is stored in libraries for further analysis (e.g., SNP swaplibraries, PRO swap libraries, and combinations thereof, among others).Selection rules of the present disclosure generate new proposed hoststrain sequences based on the predicted effect of combining elementsfrom one or more libraries for additional iterative analysis.

FIG. 6A-B depicts the DNA assembly, transformation, and strain screeningsteps of one of the embodiments of the present disclosure. FIG. 6Adepicts the steps for building DNA fragments, cloning said DNA fragmentsinto vectors, transforming said vectors into host strains, and loopingout selection sequences through counter selection. FIG. 6B depicts thesteps for high-throughput culturing, screening, and evaluation ofselected host strains. This figure also depicts the optional steps ofculturing, screening, and evaluating selected strains in culture tanks.

FIG. 7 depicts one embodiment of the automated system of the presentdisclosure. The present disclosure teaches use of automated roboticsystems with various modules capable of cloning, transforming,culturing, screening and/or sequencing host organisms.

FIG. 8 depicts an overview of an embodiment of the host strainimprovement program of the present disclosure.

FIG. 9 is a representation of the genome of Corynebacterium glutamicum,comprising around 3.2 million base pairs.

FIG. 10 depicts the results of a transformation experiment of thepresent disclosure. DNA inserts ranging from 0.5 kb to 5.0 kb weretargeted for insertion into various regions (shown as relative positions1-24) of the genome of Corynebacterium glutamicum. Light color indicatessuccessful integration, while darker color indicates insertion failure.

FIG. 11 depicts the results of a second round HTP engineering PRO swapprogram. Top promoter::gene combinations identified during the first PROswap round were analyzed according to the methods of the presentdisclosure to identify combinations of said mutations that would belikely to exhibit additive or combinatorial beneficial effects on hostperformance. Second round PRO swap mutants thus comprised paircombinations of various promoter::gene mutations. The resulting secondround mutants were screened for differences in host cell yield of aselected biomolecule. A combination pair of mutations that had beenpredicted to exhibit beneficial effects is emphasized with a circle.

FIG. 12 depicts the results of an experiment testing successful plasmidassembly for plasmids transformed into E. coli. Picking four colonies issufficient to achieve 13% failure rate for plasmids containing 1 and 2kb insertion sequences. Larger insertions may require additional colonyscreening to achieve consistent results.

FIG. 13 depicts results of an experiment testing successfultransformation of Corynebacterium glutamicum with insertion vectors. DNAinsert sizes of 2 and 5 kb exhibited high transformation rates with lowassembly failure rates.

FIG. 14 depicts results of loop out selections in Corynebacteriumglutamicum. Sucrose resistance of transformed bacteria indicates loopout of sacB selection marker. DNA insert size does not appear to impactloop out efficiency.

FIG. 15 is a similarity matrix computed using the correlation measure.The matrix is a representation of the functional similarity between SNPvariants. The consolidation of SNPs with low functional similarity isexpected to have a higher likelihood of improving strain performance, asopposed to the consolidation of SNPs with higher functional similarity.

FIG. 16A-B depicts the results of an epistasis mapping experiment.Combination of SNPs and PRO swaps with low functional similaritiesyields improved strain performance. FIG. 16A depicts a dendrogramclustered by functional similarity of all the SNPs/PRO swaps. FIG. 16Bdepicts host strain performance of consolidated SNPs as measured byproduct yield. Greater cluster distance correlates with improvedconsolidation performance of the host strain.

FIG. 17A-B depicts SNP differences among strain variants in thediversity pool. FIG. 17A depicts the relationship among the strains ofthis experiment. Strain A is the wild-type host strain. Strain B is anintermediate engineered strain. Strain C is the industrial productionstrain. FIG. 17B is a graph identifying the number of unique and sharedSNPs in each strain.

FIG. 18 depicts a first-round SNP swapping experiment according to themethods of the present disclosure. (1) all the SNPs from C will beindividually and/or combinatorially cloned into the base A strain (“waveup” A to C). (2) all the SNPs from C will be individually and/orcombinatorially removed from the commercial strain C (“wave down” C toA). (3) all the SNPs from B will be individually and/or combinatoriallycloned into the base A strain (wave up A to B). (4) all the SNPs from Bwill be individually and/or combinatorially removed from the commercialstrain B (wave down B to A). (5) all the SNPs unique to C will beindividually and/or combinatorially cloned into the commercial B strain(wave up B to C). (6) all the SNPs unique to C will be individuallyand/or combinatorially removed from the commercial strain C (wave down Cto B).

FIG. 19 illustrates example gene targets to be utilized in a promoterswap process.

FIG. 20 illustrates an exemplary promoter library that is being utilizedto conduct a promoter swap process for the identified gene targets.Promoters utilized in the PRO swap (i.e. promoter swap) process areP₁-P₈, the sequences and identity of which can be found in Table 1.

FIG. 21 illustrates that promoter swapping genetic outcomes depend onthe particular gene being targeted.

FIG. 22 depicts exemplary HTP promoter swapping data showingmodifications that significantly affect performance on lysine yield. TheX-axis represents different strains within the promoter swap geneticdesign microbial strain library, and the Y-axis includes relative lysineyield values for each strain. Each letter on the graph represents a PROswap target gene. Each data point represents a replicate. The datademonstrates that a molecular tool adapted for HTP applications, asdescribed herein (i.e. PRO swap), is able to efficiently create andoptimize microbial strain performance for the production of a compoundor molecule of interest. In this case, the compound of interest waslysine; however, the taught PRO swap molecular tool can be utilized tooptimize and/or increase the production of any compound of interest. Oneof skill in the art would understand how to choose target genes,encoding the production of a desired compound, and then utilize thetaught PRO swap procedure. One of skill in the art would readilyappreciate that the demonstrated data exemplifying lysine yieldincreases taught herein, along with the detailed disclosure presented inthe application, enables the PRO swap molecular tool to be a widelyapplicable advancement in HTP genomic engineering.

FIG. 23 illustrates the distribution of relative strain performances forthe input data under consideration. A relative performance of zeroindicates that the engineered strain performed equally well to thein-plate base strain. The processes described herein are designed toidentify the strains that are likely to perform significantly abovezero.

FIG. 24 illustrates the linear regression coefficient values, whichdepict the average change (increase or decrease) in relative strainperformance associated with each genetic change incorporated into thedepicted strains.

FIG. 25 illustrates the composition of changes for the top 100 predictedstrain designs. The x-axis lists the pool of potential genetic changes(dss mutations are SNP swaps, and Pcg mutations are PRO swaps), and they-axis shows the rank order. Black cells indicate the presence of aparticular change in the candidate design, while white cells indicatethe absence of that change. In this particular example, all of the top100 designs contain the changes pcg3121_pgi, pcg1860_pyc, dss_339, andpcg0007_39_lysa. Additionally, the top candidate design contains thechanges dss 034, dss 009.

FIG. 26 depicts the DNA assembly and transformation steps of one of theembodiments of the present disclosure. The flow chart depicts the stepsfor building DNA fragments, cloning said DNA fragments into vectors,transforming said vectors into host strains, and looping out selectionsequences through counter selection.

FIG. 27 depicts the steps for high-throughput culturing, screening, andevaluation of selected host strains. This figure also depicts theoptional steps of culturing, screening, and evaluating selected strainsin culture tanks.

FIG. 28 depicts expression profiles of illustrative promoters exhibitinga range of regulatory expression, according to the promoter ladders ofthe present disclosure. Promoter A expression peaks at the lag phase ofbacterial cultures, while promoter B and C peak at the exponential andstationary phase, respectively.

FIG. 29 depicts expression profiles of illustrative promoters exhibitinga range of regulatory expression, according to the promoter ladders ofthe present disclosure. Promoter A expression peaks immediately uponaddition of a selected substrate, but quickly returns to undetectablelevels as the concentration of the substrate is reduced. Promoter Bexpression peaks immediately upon addition of the selected substrate andlowers slowly back to undetectable levels together with thecorresponding reduction in substrate. Promoter C expression peaks uponaddition of the selected substrate, and remains highly expressedthroughout the culture, even after the substrate has dissipated.

FIG. 30 depicts expression profiles of illustrative promoters exhibitinga range of constitutive expression levels, according to the promoterladders of the present disclosure. Promoter A exhibits the lowestexpression, followed by increasing expression levels promoter B and C,respectively.

FIG. 31 diagrams an embodiment of LIMS system of the present disclosurefor strain improvement.

FIG. 32 diagrams a cloud computing implementation of embodiments of theLIMS system of the present disclosure.

FIG. 33 depicts an embodiment of the iterative predictive strain designworkflow of the present disclosure.

FIG. 34 diagrams an embodiment of a computer system, according toembodiments of the present disclosure.

FIG. 35 depicts the workflow associated with the DNA assembly accordingto one embodiment of the present disclosure. This process is divided upinto 4 stages: parts generation, plasmid assembly, plasmid QC, andplasmid preparation for transformation. During parts generation, oligosdesigned by Laboratory Information Management System (LIMS) are orderedfrom an oligo sequencing vendor and used to amplify the target sequencesfrom the host organism via PCR. These PCR parts are cleaned to removecontaminants and assessed for success by fragment analysis, in silicoquality control comparison of observed to theoretical fragment sizes,and DNA quantification. The parts are transformed into yeast along withan assembly vector and assembled into plasmids via homologousrecombination. Assembled plasmids are isolated from yeast andtransformed into E. coli for subsequent assembly quality control andamplification. During plasmid assembly quality control, severalreplicates of each plasmid are isolated, amplified using Rolling CircleAmplification (RCA), and assessed for correct assembly by enzymaticdigest and fragment analysis. Correctly assembled plasmids identifiedduring the QC process are hit picked to generate permanent stocks andthe plasmid DNA extracted and quantified prior to transformation intothe target host organism.

FIG. 36 depicts the results of an experiment characterizing the effectsof Terminators T₁-T₈ in two media over two time points. Conditions A andC represent the two time points for the BHI media, while the B and Dpoints represent the two time points for the HTP test media.

FIG. 37 depicts the results of an experiment comparing the effectivenessof traditional strain improvement approaches such as UV mutagenesisagainst the HTP engineering methodologies of the present disclosure. Thevast majority of UV mutations produced no noticeable increase in hostcell performance. In contrast, PRO swap methodologies of the presentdisclosure produced a high proportion of mutants exhibiting 1.2 to 2fold increases in host cell performance.

FIG. 38 depicts the results of a first round HTP engineering SNP swapprogram. 186 individual SNP mutations were identified and individuallycloned onto a base strain. The resulting mutants were screened fordifferences in host cell yield of a selected biomolecule.

FIG. 39 depicts the results of a second round HTP engineering SNP swapprogram. 176 individual SNP mutations from a first round SNP swapprogram were individually cloned into a second round host cell straincontaining a beneficial SNP identified during a first round SNP program.The resulting mutants thus represent the effect of two mutationcombination pairs. Screening results for differences in host cell yield(Y-axis) and productivity (X-axis) for the selected biomolecule areshown.

FIG. 40 depicts the results of a tank fermentation validationexperiment. The top mutation pairs from the second round of HTP SNP swapwere cultured in fermentation tanks. Results for host cell yield andproductivity for the selected biomolecule (i.e. lysine) are shown. Ascan be seen, in one round of genomic engineering the inventors utilizedthe PRO swap procedure to determine that a particular PRO swap mutant(zwf) exhibited increased yield of a selected biomolecule compared tobase strain (i.e. compare base strain to base strain+zwf). Then, theinventors performed another round of genomic engineering, wherein a SNPswap procedure was used to determine beneficial SNP mutations that couldaffect yield of the biomolecule, when combined with said PRO swapmutant. The combination of the PRO swap procedure and SNP swap procedurecreated mutants with even higher yields than the previous PRO swap onlymutants (i.e. compare base strain+zwf+SNP121 to the previously discussedbase strain+zwf). This figure illustrates the dramatic improvements inyield that can be achieved by combining the PRO swap and SNP swapprocedures of the disclosure. In aspects, combining a PRO swap genomicengineering campaign with a SNP swap genomic engineering campaign canlead to increased yield and/or productivity of a biomolecule/product ofinterest by a factor of 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9% 10%, 15%,20%, 25%, 30%, 40%, 45%, 50%, or more, relative to a base strain.

FIG. 41 depicts the results of a first round HTP engineering PRO swapprogram. Selected genes believed to be associated with host performancewere combined with a promoter ladder to create a first round PRO swaplibrary, according to the methods of the present disclosure. Theresulting mutants were screened for differences in host cell yield of aselected biomolecule (i.e. lysine).

FIG. 42 is a flowchart illustrating the consideration of epistaticeffects in the selection of mutations for the design of a microbialstrain, according to embodiments of the disclosure.

FIG. 43A-B depicts the results of A. niger transformation and validationaccording to the methods of the present disclosure. FIG. 43A—is apicture of a 96-well media plate of A. niger transformants. Transformedcultures comprise a mutation in the aygA, which causes the cells toappear lighter yellow instead of black (transformed wells are circled inwhite). FIG. 43B—depicts the results of next generation sequencing oftransformed A. niger mutants. The X-axis represents the target DNA'ssequence identity with the untransformed parent strain. The Y-axisrepresents the target DNA's sequence identity with the expectedmutation. Data points towards the bottom right of the chart exhibit highsimilarity with the parent strain, and low similarity with the expectedtransformed sequences. Data points towards the top left of the chartexhibit high similarity to expected transformed sequences and lowidentity with parent strain. Data points in the middle likely representheterokaryons with multiple nuclei.

FIG. 44A-B illustrates a SNP swap implementation in A. niger. FIG.44A—illustrates the designed genetic edits for each SNP of the SNP swap.The figure further illustrates the cotransformation in which the pyrGgene is introduced into the locus for the aygA wild type gene. FIG.44B—are two pictures of the 96-well media plates for screening the A.niger transformants. Light yellow colonies represent transformants inwhich the aygA gene has been successfully disrupted.

FIG. 45 depicts a quality control (QC) chart identifying successful A.niger mutant transformants (top box) based on next generation sequencingresults. Overall 29.2% of yellow colonies selected from the cultureplates exhibit the expected SNP genetic change.

FIG. 46 Depicts the results of next generation sequencing of transformedA. niger mutants. The X-axis represents the target DNA's sequenceidentity with the untransformed parent strain. The Y-axis represents thetarget DNA's sequence identity with the expected mutation. Data pointstowards the bottom right of the chart exhibit high similarity with theparent strain, and low similarity with the expected transformedsequences. Data points towards the top left of the chart exhibit highsimilarity to expected transformed sequences and low identity withparent strain. Data points in the middle likely represent heterokaryonswith multiple nuclei.

FIG. 47 is a dot plot for the predicted performance vs measuredperformance of training data for a yield model of the presentdisclosure. The underlying model is a Kernel Ridge Regression model(with 4th order polynomial kernel). The model is trained on 1864 uniquegenetic constructs and associated phenotypic performance. The fittedmodel has an r2 value of 0.52.

FIG. 48 Depicts the genetic makeup of candidate designs generated by theprediction algorithms of the present disclosure. These candidate designswere submitted for HTP build and analysis. Here the candidate design isdefined as the combination of parent strain id and introducedmutation(s).

FIG. 49 is a dot plot of the predicted performance vs. measuredperformance of candidate designs generated by the prediction algorithmsof the present disclosure, and built according the HTP build methods ofthe present disclosure. This figure demonstrates that the model maypredict candidate strain performance within an acceptable degree ofaccuracy.

FIG. 50 is a box and whiskers plot depicting the yield percent change ofcandidate strains with respect to parent strains. On the y-axis, a valueof 0.01 corresponds to 1%. This figure demonstrates that strainsdesigned by a computer model (light gray) achieve measureableimprovement over their corresponding parent strains. Additionally, thefigure demonstrates that these model base strain improvements arecomparable in magnitude to improvements achieved by human expertdesigned strains.

FIG. 51 illustrates the yield performance distribution for strainsdesigned by the computer model (dark grey) and by a human expert (lightgrey). Computer-designed strains exhibited tighter distributions withhigher median gains.

FIG. 52 is a box and whiskers plot depicting the absolute yield ofcandidate strains generated by the computer (light grey) or by a humanexpert (dark grey). Results are aggregated by parent strain.

DETAILED DESCRIPTION Definitions

While the following terms are believed to be well understood by one ofordinary skill in the art, the following definitions are set forth tofacilitate explanation of the presently disclosed subject matter.

The term “a” or “an” refers to one or more of that entity, i.e. canrefer to a plural referents. As such, the terms “a” or “an”, “one ormore” and “at least one” are used interchangeably herein. In addition,reference to “an element” by the indefinite article “a” or “an” does notexclude the possibility that more than one of the elements is present,unless the context clearly requires that there is one and only one ofthe elements.

As used herein the terms “cellular organism” “microorganism” or“microbe” should be taken broadly. These terms are used interchangeablyand include, but are not limited to, the two prokaryotic domains,Bacteria and Archaea, as well as certain eukaryotic fungi and protists.In some embodiments, the disclosure refers to the “microorganisms” or“cellular organisms” or “microbes” of lists/tables and figures presentin the disclosure. This characterization can refer to not only theidentified taxonomic genera of the tables and figures, but also theidentified taxonomic species, as well as the various novel and newlyidentified or designed strains of any organism in said tables orfigures. The same characterization holds true for the recitation ofthese terms in other parts of the Specification, such as in theExamples.

The term “prokaryotes” is art recognized and refers to cells whichcontain no nucleus or other cell organelles. The prokaryotes aregenerally classified in one of two domains, the Bacteria and theArchaea. The definitive difference between organisms of the Archaea andBacteria domains is based on fundamental differences in the nucleotidebase sequence in the 16S ribosomal RNA.

The term “Archaea” refers to a categorization of organisms of thedivision Mendosicutes, typically found in unusual environments anddistinguished from the rest of the prokaryotes by several criteria,including the number of ribosomal proteins and the lack of muramic acidin cell walls. On the basis of ssrRNA analysis, the Archaea consist oftwo phylogenetically-distinct groups: Crenarchaeota and Euryarchaeota.On the basis of their physiology, the Archaea can be organized intothree types: methanogens (prokaryotes that produce methane); extremehalophiles (prokaryotes that live at very high concentrations of salt(NaCl); and extreme (hyper) thermophilus (prokaryotes that live at veryhigh temperatures). Besides the unifying archaeal features thatdistinguish them from Bacteria (i.e., no murein in cell wall,ester-linked membrane lipids, etc.), these prokaryotes exhibit uniquestructural or biochemical attributes which adapt them to theirparticular habitats. The Crenarchaeota consists mainly ofhyperthermophilic sulfur-dependent prokaryotes and the Euryarchaeotacontains the methanogens and extreme halophiles.

“Bacteria” or “eubacteria” refers to a domain of prokaryotic organisms.Bacteria include at least 11 distinct groups as follows: (1)Gram-positive (gram+) bacteria, of which there are two majorsubdivisions: (1) high G+C group (Actinomycetes, Mycobacteria,Micrococcus, others) (2) low G+C group (Bacillus, Clostridia,Lactobacillus, Staphylococci, Streptococci, Mycoplasmas); (2)Proteobacteria, e.g., Purple photosynthetic+non-photosyntheticGram-negative bacteria (includes most “common” Gram-negative bacteria);(3) Cyanobacteria, e.g., oxygenic phototrophs; (4) Spirochetes andrelated species; (5) Planctomyces; (6) Bacteroides, Flavobacteria; (7)Chlamydia; (8) Green sulfur bacteria; (9) Green non-sulfur bacteria(also anaerobic phototrophs); (10) Radioresistant micrococci andrelatives; (11) Thermotoga and Thermosipho thermophiles.

A “eukaryote” is any organism whose cells contain a nucleus and otherorganelles enclosed within membranes. Eukaryotes belong to the taxonEukarya or Eukaryota. The defining feature that sets eukaryotic cellsapart from prokaryotic cells (the aforementioned Bacteria and Archaea)is that they have membrane-bound organelles, especially the nucleus,which contains the genetic material, and is enclosed by the nuclearenvelope.

The terms “genetically modified host cell,” “recombinant host cell,” and“recombinant strain” are used interchangeably herein and refer to hostcells that have been genetically modified by the cloning andtransformation methods of the present disclosure. Thus, the termsinclude a host cell (e.g., bacteria, yeast cell, fungal cell, CHO, humancell, etc.) that has been genetically altered, modified, or engineered,such that it exhibits an altered, modified, or different genotype and/orphenotype (e.g., when the genetic modification affects coding nucleicacid sequences of the microorganism), as compared to thenaturally-occurring organism from which it was derived. It is understoodthat in some embodiments, the terms refer not only to the particularrecombinant host cell in question, but also to the progeny or potentialprogeny of such a host cell

The term “wild-type microorganism” or “wild-type host cell” describes acell that occurs in nature, i.e. a cell that has not been geneticallymodified.

The term “genetically engineered” may refer to any manipulation of ahost cell's genome (e.g. by insertion, deletion, mutation, orreplacement of nucleic acids).

The term “control” or “control host cell” refers to an appropriatecomparator host cell for determining the effect of a geneticmodification or experimental treatment. In some embodiments, the controlhost cell is a wild type cell. In other embodiments, a control host cellis genetically identical to the genetically modified host cell, save forthe genetic modification(s) differentiating the treatment host cell. Insome embodiments, the present disclosure teaches the use of parentstrains as control host cells (e.g., the S₁ strain that was used as thebasis for the strain improvement program). In other embodiments, a hostcell may be a genetically identical cell that lacks a specific promoteror SNP being tested in the treatment host cell.

As used herein, the term “allele(s)” means any of one or morealternative forms of a gene, all of which alleles relate to at least onetrait or characteristic. In a diploid cell, the two alleles of a givengene occupy corresponding loci on a pair of homologous chromosomes.

As used herein, the term “locus” (loci plural) means a specific place orplaces or a site on a chromosome where for example a gene or geneticmarker is found.

As used herein, the term “genetically linked” refers to two or moretraits that are co-inherited at a high rate during breeding such thatthey are difficult to separate through crossing.

A “recombination” or “recombination event” as used herein refers to achromosomal crossing over or independent assortment.

As used herein, the term “phenotype” refers to the observablecharacteristics of an individual cell, cell culture, organism, or groupof organisms which results from the interaction between thatindividual's genetic makeup (i.e., genotype) and the environment.

As used herein, the term “chimeric” or “recombinant” when describing anucleic acid sequence or a protein sequence refers to a nucleic acid, ora protein sequence, that links at least two heterologouspolynucleotides, or two heterologous polypeptides, into a singlemacromolecule, or that re-arranges one or more elements of at least onenatural nucleic acid or protein sequence. For example, the term“recombinant” can refer to an artificial combination of two otherwiseseparated segments of sequence, e.g., by chemical synthesis or by themanipulation of isolated segments of nucleic acids by geneticengineering techniques.

As used herein, a “synthetic nucleotide sequence” or “syntheticpolynucleotide sequence” is a nucleotide sequence that is not known tooccur in nature or that is not naturally occurring. Generally, such asynthetic nucleotide sequence will comprise at least one nucleotidedifference when compared to any other naturally occurring nucleotidesequence.

As used herein, the term “nucleic acid” refers to a polymeric form ofnucleotides of any length, either ribonucleotides ordeoxyribonucleotides, or analogs thereof. This term refers to theprimary structure of the molecule, and thus includes double- andsingle-stranded DNA, as well as double- and single-stranded RNA. It alsoincludes modified nucleic acids such as methylated and/or capped nucleicacids, nucleic acids containing modified bases, backbone modifications,and the like. The terms “nucleic acid” and “nucleotide sequence” areused interchangeably.

As used herein, the term “gene” refers to any segment of DNA associatedwith a biological function. Thus, genes include, but are not limited to,coding sequences and/or the regulatory sequences required for theirexpression. Genes can also include non-expressed DNA segments that, forexample, form recognition sequences for other proteins. Genes can beobtained from a variety of sources, including cloning from a source ofinterest or synthesizing from known or predicted sequence information,and may include sequences designed to have desired parameters.

As used herein, the term “homologous” or “homologue” or “ortholog” isknown in the art and refers to related sequences that share a commonancestor or family member and are determined based on the degree ofsequence identity. The terms “homology,” “homologous,” “substantiallysimilar” and “corresponding substantially” are used interchangeablyherein. They refer to nucleic acid fragments wherein changes in one ormore nucleotide bases do not affect the ability of the nucleic acidfragment to mediate gene expression or produce a certain phenotype.These terms also refer to modifications of the nucleic acid fragments ofthe instant disclosure such as deletion or insertion of one or morenucleotides that do not substantially alter the functional properties ofthe resulting nucleic acid fragment relative to the initial, unmodifiedfragment. It is therefore understood, as those skilled in the art willappreciate, that the disclosure encompasses more than the specificexemplary sequences. These terms describe the relationship between agene found in one species, subspecies, variety, cultivar or strain andthe corresponding or equivalent gene in another species, subspecies,variety, cultivar or strain. For purposes of this disclosure homologoussequences are compared. “Homologous sequences” or “homologues” or“orthologs” are thought, believed, or known to be functionally related.A functional relationship may be indicated in any one of a number ofways, including, but not limited to: (a) degree of sequence identityand/or (b) the same or similar biological function. Preferably, both (a)and (b) are indicated. Homology can be determined using softwareprograms readily available in the art, such as those discussed inCurrent Protocols in Molecular Biology (F. M. Ausubel et al., eds.,1987) Supplement 30, section 7.718, Table 7.71. Some alignment programsare MacVector (Oxford Molecular Ltd, Oxford, U.K.), ALIGN Plus(Scientific and Educational Software, Pennsylvania) and AlignX (VectorNTI, Invitrogen, Carlsbad, Calif.). Another alignment program isSequencher (Gene Codes, Ann Arbor, Mich.), using default parameters.

As used herein, the term “endogenous” or “endogenous gene,” refers tothe naturally occurring gene, in the location in which it is naturallyfound within the host cell genome. In the context of the presentdisclosure, operably linking a heterologous promoter to an endogenousgene means genetically inserting a heterologous promoter sequence infront of an existing gene, in the location where that gene is naturallypresent. An endogenous gene as described herein can include alleles ofnaturally occurring genes that have been mutated according to any of themethods of the present disclosure.

As used herein, the term “exogenous” is used interchangeably with theterm “heterologous,” and refers to a substance coming from some sourceother than its native source. For example, the terms “exogenousprotein,” or “exogenous gene” refer to a protein or gene from anon-native source or location, and that have been artificially suppliedto a biological system.

As used herein, the term “nucleotide change” refers to, e.g., nucleotidesubstitution, deletion, and/or insertion, as is well understood in theart. For example, mutations contain alterations that produce silentsubstitutions, additions, or deletions, but do not alter the propertiesor activities of the encoded protein or how the proteins are made.

As used herein, the term “protein modification” refers to, e.g., aminoacid substitution, amino acid modification, deletion, and/or insertion,as is well understood in the art.

As used herein, the term “at least a portion” or “fragment” of a nucleicacid or polypeptide means a portion having the minimal sizecharacteristics of such sequences, or any larger fragment of the fulllength molecule, up to and including the full length molecule. Afragment of a polynucleotide of the disclosure may encode a biologicallyactive portion of a genetic regulatory element. A biologically activeportion of a genetic regulatory element can be prepared by isolating aportion of one of the polynucleotides of the disclosure that comprisesthe genetic regulatory element and assessing activity as describedherein. Similarly, a portion of a polypeptide may be 4 amino acids, 5amino acids, 6 amino acids, 7 amino acids, and so on, going up to thefull length polypeptide. The length of the portion to be used willdepend on the particular application. A portion of a nucleic acid usefulas a hybridization probe may be as short as 12 nucleotides; in someembodiments, it is 20 nucleotides. A portion of a polypeptide useful asan epitope may be as short as 4 amino acids. A portion of a polypeptidethat performs the function of the full-length polypeptide wouldgenerally be longer than 4 amino acids.

Variant polynucleotides also encompass sequences derived from amutagenic and recombinogenic procedure such as DNA shuffling. Strategiesfor such DNA shuffling are known in the art. See, for example, Stemmer(1994) PNAS 91:10747-10751; Stemmer (1994) Nature 370:389-391; Crameriet al. (1997) Nature Biotech. 15:436-438; Moore et al. (1997) J. Mol.Biol. 272:336-347; Zhang et al. (1997) PNAS 94:4504-4509; Crameri et al.(1998) Nature 391:288-291; and U.S. Pat. Nos. 5,605,793 and 5,837,458.

For PCR amplifications of the polynucleotides disclosed herein,oligonucleotide primers can be designed for use in PCR reactions toamplify corresponding DNA sequences from cDNA or genomic DNA extractedfrom any organism of interest. Methods for designing PCR primers and PCRcloning are generally known in the art and are disclosed in Sambrook etal. (2001) Molecular Cloning: A Laboratory Manual (3^(rd) ed., ColdSpring Harbor Laboratory Press, Plainview, N.Y.). See also Innis et al.,eds. (1990) PCR Protocols: A Guide to Methods and Applications (AcademicPress, New York); Innis and Gelfand, eds. (1995) PCR Strategies(Academic Press, New York); and Innis and Gelfand, eds. (1999) PCRMethods Manual (Academic Press, New York). Known methods of PCR include,but are not limited to, methods using paired primers, nested primers,single specific primers, degenerate primers, gene-specific primers,vector-specific primers, partially-mismatched primers, and the like.

The term “primer” as used herein refers to an oligonucleotide which iscapable of annealing to the amplification target allowing a DNApolymerase to attach, thereby serving as a point of initiation of DNAsynthesis when placed under conditions in which synthesis of primerextension product is induced, i.e., in the presence of nucleotides andan agent for polymerization such as DNA polymerase and at a suitabletemperature and pH. The (amplification) primer is preferably singlestranded for maximum efficiency in amplification. Preferably, the primeris an oligodeoxyribonucleotide. The primer must be sufficiently long toprime the synthesis of extension products in the presence of the agentfor polymerization. The exact lengths of the primers will depend on manyfactors, including temperature and composition (A/T vs. G/C content) ofprimer. A pair of bi-directional primers consists of one forward and onereverse primer as commonly used in the art of DNA amplification such asin PCR amplification.

As used herein, “promoter” refers to a DNA sequence capable ofcontrolling the expression of a coding sequence or functional RNA. Insome embodiments, the promoter sequence consists of proximal and moredistal upstream elements, the latter elements often referred to asenhancers. Accordingly, an “enhancer” is a DNA sequence that canstimulate promoter activity, and may be an innate element of thepromoter or a heterologous element inserted to enhance the level ortissue specificity of a promoter. Promoters may be derived in theirentirety from a native gene, or be composed of different elementsderived from different promoters found in nature, or even comprisesynthetic DNA segments. It is understood by those skilled in the artthat different promoters may direct the expression of a gene indifferent tissues or cell types, or at different stages of development,or in response to different environmental conditions. It is furtherrecognized that since in most cases the exact boundaries of regulatorysequences have not been completely defined, DNA fragments of somevariation may have identical promoter activity.

As used herein, the phrases “recombinant construct”, “expressionconstruct”, “chimeric construct”, “construct”, and “recombinant DNAconstruct” are used interchangeably herein. A recombinant constructcomprises an artificial combination of nucleic acid fragments, e.g.,regulatory and coding sequences that are not found together in nature.For example, a chimeric construct may comprise regulatory sequences andcoding sequences that are derived from different sources, or regulatorysequences and coding sequences derived from the same source, butarranged in a manner different than that found in nature. Such constructmay be used by itself or may be used in conjunction with a vector. If avector is used then the choice of vector is dependent upon the methodthat will be used to transform host cells as is well known to thoseskilled in the art. For example, a plasmid vector can be used. Theskilled artisan is well aware of the genetic elements that must bepresent on the vector in order to successfully transform, select andpropagate host cells comprising any of the isolated nucleic acidfragments of the disclosure. The skilled artisan will also recognizethat different independent transformation events will result indifferent levels and patterns of expression (Jones et al., (1985) EMBOJ. 4:2411-2418; De Almeida et al., (1989) Mol. Gen. Genetics 218:78-86),and thus that multiple events must be screened in order to obtain linesdisplaying the desired expression level and pattern. Such screening maybe accomplished by Southern analysis of DNA, Northern analysis of mRNAexpression, immunoblotting analysis of protein expression, or phenotypicanalysis, among others. Vectors can be plasmids, viruses,bacteriophages, pro-viruses, phagemids, transposons, artificialchromosomes, and the like, that replicate autonomously or can integrateinto a chromosome of a host cell. A vector can also be a naked RNApolynucleotide, a naked DNA polynucleotide, a polynucleotide composed ofboth DNA and RNA within the same strand, a poly-lysine-conjugated DNA orRNA, a peptide-conjugated DNA or RNA, a liposome-conjugated DNA, or thelike, that is not autonomously replicating. As used herein, the term“expression” refers to the production of a functional end-product e.g.,an mRNA or a protein (precursor or mature).

“Operably linked” means in this context the sequential arrangement ofthe promoter polynucleotide according to the disclosure with a furtheroligo- or polynucleotide, resulting in transcription of said furtherpolynucleotide.

The term “product of interest” or “biomolecule” as used herein refers toany product produced by microbes from feedstock. In some cases, theproduct of interest may be a small molecule, enzyme, peptide, aminoacid, organic acid, synthetic compound, fuel, alcohol, etc. For example,the product of interest or biomolecule may be any primary or secondaryextracellular metabolite. The primary metabolite may be, inter alia,ethanol, citric acid, lactic acid, glutamic acid, glutamate, lysine,threonine, tryptophan and other amino acids, vitamins, polysaccharides,etc. The secondary metabolite may be, inter alia, an antibiotic compoundlike penicillin, or an immunosuppressant like cyclosporin A, a planthormone like gibberellin, a statin drug like lovastatin, a fungicidelike griseofulvin, etc. The product of interest or biomolecule may alsobe any intracellular component produced by a microbe, such as: amicrobial enzyme, including: catalase, amylase, protease, pectinase,glucose isomerase, cellulase, hemicellulase, lipase, lactase,streptokinase, and many others. The intracellular component may alsoinclude recombinant proteins, such as: insulin, hepatitis B vaccine,interferon, granulocyte colony-stimulating factor, streptokinase andothers.

The term “carbon source” generally refers to a substance suitable to beused as a source of carbon for cell growth. Carbon sources include, butare not limited to, biomass hydrolysates, starch, sucrose, cellulose,hemicellulose, xylose, and lignin, as well as monomeric components ofthese substrates. Carbon sources can comprise various organic compoundsin various forms, including, but not limited to polymers, carbohydrates,acids, alcohols, aldehydes, ketones, amino acids, peptides, etc. Theseinclude, for example, various monosaccharides such as glucose, dextrose(D-glucose), maltose, oligosaccharides, polysaccharides, saturated orunsaturated fatty acids, succinate, lactate, acetate, ethanol, etc., ormixtures thereof. Photosynthetic organisms can additionally produce acarbon source as a product of photosynthesis. In some embodiments,carbon sources may be selected from biomass hydrolysates and glucose.

The term “feedstock” is defined as a raw material or mixture of rawmaterials supplied to a microorganism or fermentation process from whichother products can be made. For example, a carbon source, such asbiomass or the carbon compounds derived from biomass are a feedstock fora microorganism that produces a product of interest (e.g. smallmolecule, peptide, synthetic compound, fuel, alcohol, etc.) in afermentation process. However, a feedstock may contain nutrients otherthan a carbon source.

The term “volumetric productivity” or “production rate” is defined asthe amount of product formed per volume of medium per unit of time.Volumetric productivity can be reported in gram per liter per hour(g/L/h).

The term “specific productivity” is defined as the rate of formation ofthe product. Specific productivity is herein further defined as thespecific productivity in gram product per gram of cell dry weight (CDW)per hour (g/g CDW/h). Using the relation of CDW to OD₆₀₀ for the givenmicroorganism specific productivity can also be expressed as gramproduct per liter culture medium per optical density of the culturebroth at 600 nm (OD) per hour (g/L/h/OD).

The term “yield” is defined as the amount of product obtained per unitweight of raw material and may be expressed as g product per g substrate(g/g). Yield may be expressed as a percentage of the theoretical yield.“Theoretical yield” is defined as the maximum amount of product that canbe generated per a given amount of substrate as dictated by thestoichiometry of the metabolic pathway used to make the product.

The term “titre” or “titer” is defined as the strength of a solution orthe concentration of a substance in solution. For example, the titre ofa product of interest (e.g. small molecule, peptide, synthetic compound,fuel, alcohol, etc.) in a fermentation broth is described as g ofproduct of interest in solution per liter of fermentation broth (g/L).

The term “total titer” is defined as the sum of all product of interestproduced in a process, including but not limited to the product ofinterest in solution, the product of interest in gas phase ifapplicable, and any product of interest removed from the process andrecovered relative to the initial volume in the process or the operatingvolume in the process

As used herein, the term “HTP genetic design library” or “library”refers to collections of genetic perturbations according to the presentdisclosure. In some embodiments, the libraries of the present inventionmay manifest as i) a collection of sequence information in a database orother computer file, ii) a collection of genetic constructs encoding forthe aforementioned series of genetic elements, or iii) host cell strainscomprising said genetic elements. In some embodiments, the libraries ofthe present disclosure may refer to collections of individual elements(e.g., collections of promoters for PRO swap libraries, or collectionsof terminators for STOP swap libraries). In other embodiments, thelibraries of the present disclosure may also refer to combinations ofgenetic elements, such as combinations of promoter::genes,gene:terminator, or even promoter:gene:terminators. In some embodiments,the libraries of the present disclosure further comprise meta dataassociated with the effects of applying each member of the library inhost organisms. For example, a library as used herein can include acollection of promoter::gene sequence combinations, together with theresulting effect of those combinations on one or more phenotypes in aparticular species, thus improving the future predictive value of usingsaid combination in future promoter swaps.

As used herein, the term “SNP” refers to Small Nuclear Polymorphism(s).In some embodiments, SNPs of the present disclosure should be construedbroadly, and include single nucleotide polymorphisms, sequenceinsertions, deletions, inversions, and other sequence replacements. Asused herein, the term “non-synonymous” or non-synonymous SNPs” refers tomutations that lead to coding changes in host cell proteins

A “high-throughput (HTP)” method of genomic engineering may involve theutilization of at least one piece of automated equipment (e.g. a liquidhandler or plate handler machine) to carry out at least one step of saidmethod.

Traditional Methods of Strain Improvement

Traditional approaches to strain improvement can be broadly categorizedinto two types of approaches: directed strain engineering, and randommutagenesis.

Directed engineering methods of strain improvement involve the plannedperturbation of a handful of genetic elements of a specific organism.These approaches are typically focused on modulating specificbiosynthetic or developmental programs, and rely on prior knowledge ofthe genetic and metabolic factors affecting said pathways. In itssimplest embodiments, directed engineering involves the transfer of acharacterized trait (e.g., gene, promoter, or other genetic elementcapable of producing a measurable phenotype) from one organism toanother organism of the same, or different species.

Random approaches to strain engineering involve the random mutagenesisof parent strains, coupled with extensive screening designed to identifyperformance improvements. Approaches to generating these randommutations include exposure to ultraviolet radiation, or mutagenicchemicals such as Ethyl methanesulfonate. Though random and largelyunpredictable, this traditional approach to strain improvement hadseveral advantages compared to more directed genetic manipulations.First, many industrial organisms were (and remain) poorly characterizedin terms of their genetic and metabolic repertoires, renderingalternative directed improvement approaches difficult, if notimpossible.

Second, even in relatively well characterized systems, genotypic changesthat result in industrial performance improvements are difficult topredict, and sometimes only manifest themselves as epistatic phenotypesrequiring cumulative mutations in many genes of known and unknownfunction.

Additionally, for many years, the genetic tools required for makingdirected genomic mutations in a given industrial organism wereunavailable, or very slow and/or difficult to use.

The extended application of the traditional strain improvement programs,however, yield progressively reduced gains in a given strain lineage,and ultimately lead to exhausted possibilities for further strainefficiencies. Beneficial random mutations are relatively rare events,and require large screening pools and high mutation rates. Thisinevitably results in the inadvertent accumulation of many neutraland/or detrimental (or partly detrimental) mutations in “improved”strains, which ultimately create a drag on future efficiency gains.

Another limitation of traditional cumulative improvement approaches isthat little to no information is known about any particular mutation'seffect on any strain metric. This fundamentally limits a researcher'sability to combine and consolidate beneficial mutations, or to removeneutral or detrimental mutagenic “baggage.”

Other approaches and technologies exist to randomly recombine mutationsbetween strains within a mutagenic lineage. For example, some formatsand examples for iterative sequence recombination, sometimes referred toas DNA shuffling, evolution, or molecular breeding, have been describedin U.S. patent application Ser. No. 08/198,431, filed Feb. 17, 1994,Serial No. PCT/US95/02126, filed, Feb. 17, 1995, Ser. No. 08/425,684,filed Apr. 18, 1995, Ser. No. 08/537,874, filed Oct. 30, 1995, Ser. No.08/564,955, filed Nov. 30, 1995, Ser. No. 08/621,859, filed. Mar. 25,1996, Ser. No. 08/621,430, filed Mar. 25, 1996, Serial No.PCT/US96/05480, filed Apr. 18, 1996, Ser. No. 08/650,400, filed May 20,1996, Ser. No. 08/675,502, filed Jul. 3, 1996, Ser. No. 08/721,824,filed Sep. 27, 1996, and Ser. No. 08/722,660 filed Sep. 27, 1996;Stemmer, Science 270:1510 (1995); Stemmer et al., Gene 164:49-53 (1995);Stemmer, Bio/Technology 13:549-553 (1995); Stemmer, Proc. Natl. Acad.Sci. U.S.A. 91:10747-10751 (1994); Stemmer, Nature 370:389-391 (1994);Crameri et al., Nature Medicine 2(1):1-3 (1996); Crameri et al., NatureBiotechnology 14:315-319 (1996), each of which is incorporated herein byreference in its entirety for all purposes.

These include techniques such as protoplast fusion and whole genomeshuffling that facilitate genomic recombination across mutated strains.For some industrial microorganisms such as yeast and filamentous fungi,natural mating cycles can also be exploited for pairwise genomicrecombination. In this way, detrimental mutations can be removed by‘back-crossing’ mutants with parental strains and beneficial mutationsconsolidated. Moreover, beneficial mutations from two different strainlineages can potentially be combined, which creates additionalimprovement possibilities over what might be available from mutating asingle strain lineage on its own. However, these approaches are subjectto many limitations that are circumvented using the methods of thepresent disclosure.

For example, traditional recombinant approaches as described above areslow and rely on a relatively small number of random recombinationcrossover events to swap mutations, and are therefore limited in thenumber of combinations that can be attempted in any given cycle, or timeperiod. In addition, although the natural recombination events in theprior art are essentially random, they are also subject to genomepositional bias.

Most importantly, the traditional approaches also provide littleinformation about the influence of individual mutations and due to therandom distribution of recombined mutations many specific combinationscannot be generated and evaluated.

To overcome many of the aforementioned problems associated withtraditional strain improvement programs, the present disclosure setsforth a unique HTP genomic engineering platform that is computationallydriven and integrates molecular biology, automation, data analytics, andmachine learning protocols. This integrative platform utilizes a suiteof HTP molecular tool sets that are used to construct HTP genetic designlibraries. These genetic design libraries will be elaborated upon below.

The taught HTP platform and its unique microbial genetic designlibraries fundamentally shift the paradigm of microbial straindevelopment and evolution. For example, traditional mutagenesis-basedmethods of developing an industrial microbial strain will eventuallylead to microbes burdened with a heavy mutagenic load that has beenaccumulated over years of random mutagenesis.

The ability to solve this issue (i.e. remove the genetic baggageaccumulated by these microbes) has eluded microbial researchers fordecades. However, utilizing the HTP platform disclosed herein, theseindustrial strains can be “rehabilitated,” and the genetic mutationsthat are deleterious can be identified and removed. Congruently, thegenetic mutations that are identified as beneficial can be kept, and insome cases improved upon. The resulting microbial strains demonstratesuperior phenotypic traits (e.g., improved production of a compound ofinterest), as compared to their parental strains.

Furthermore, the HTP platform taught herein is able to identify,characterize, and quantify the effect that individual mutations have onmicrobial strain performance. This information, i.e. what effect does agiven genetic change x have on host cell phenotype y (e.g., productionof a compound or product of interest), is able to be generated and thenstored in the microbial HTP genetic design libraries discussed below.That is, sequence information for each genetic permutation, and itseffect on the host cell phenotype are stored in one or more databases,and are available for subsequent analysis (e.g., epistasis mapping, asdiscussed below). The present disclosure also teaches methods ofphysically saving/storing valuable genetic permutations in the form ofgenetic insertion constructs, or in the form of one or more host cellorganisms containing said genetic permutation (e.g., see librariesdiscussed below.)

When one couples these HTP genetic design libraries into an iterativeprocess that is integrated with a sophisticated data analytics andmachine learning process a dramatically different methodology forimproving host cells emerges. The taught platform is thereforefundamentally different from the previously discussed traditionalmethods of developing host cell strains. The taught HTP platform doesnot suffer from many of the drawbacks associated with the previousmethods. These and other advantages will become apparent with referenceto the HTP molecular tool sets and the derived genetic design librariesdiscussed below.

Genetic Design & Microbial Engineering: A Systematic CombinatorialApproach to Strain Improvement Utilizing a Suite of HTP Molecular Toolsand HTP Genetic Design Libraries

As aforementioned, the present disclosure provides a novel HTP platformand genetic design strategy for engineering microbial organisms throughiterative systematic introduction and removal of genetic changes acrossstrains. The platform is supported by a suite of molecular tools, whichenable the creation of HTP genetic design libraries and allow for theefficient implementation of genetic alterations into a given hoststrain.

The HTP genetic design libraries of the disclosure serve as sources ofpossible genetic alterations that may be introduced into a particularmicrobial strain background. In this way, the HTP genetic designlibraries are repositories of genetic diversity, or collections ofgenetic perturbations, which can be applied to the initial or furtherengineering of a given microbial strain. Techniques for programminggenetic designs for implementation to host strains are described inpending U.S. patent application Ser. No. 15/140,296, entitled “MicrobialStrain Design System and Methods for Improved Large Scale Production ofEngineered Nucleotide Sequences,” incorporated by reference in itsentirety herein.

The HTP molecular tool sets utilized in this platform may include, interalia: (1) Promoter swaps (PRO Swap), (2) SNP swaps, (3) Start/Stop codonexchanges, (4) STOP swaps, and (5) Sequence optimization. The HTPmethods of the present disclosure also teach methods for directing theconsolidation/combinatorial use of HTP tool sets, including (6)Epistasis mapping protocols. As aforementioned, this suite of moleculartools, either in isolation or combination, enables the creation of HTPgenetic design host cell libraries.

As will be demonstrated, utilization of the aforementioned HTP geneticdesign libraries in the context of the taught HTP microbial engineeringplatform enables the identification and consolidation of beneficial“causative” mutations or gene sections and also the identification andremoval of passive or detrimental mutations or gene sections. This newapproach allows rapid improvements in strain performance that could notbe achieved by traditional random mutagenesis or directed geneticengineering. The removal of genetic burden or consolidation ofbeneficial changes into a strain with no genetic burden also provides anew, robust starting point for additional random mutagenesis that mayenable further improvements.

In some embodiments, the present disclosure teaches that as orthogonalbeneficial changes are identified across various, discrete branches of amutagenic strain lineage, they can also be rapidly consolidated intobetter performing strains. These mutations can also be consolidated intostrains that are not part of mutagenic lineages, such as strains withimprovements gained by directed genetic engineering.

In some embodiments, the present disclosure differs from known strainimprovement approaches in that it analyzes the genome-wide combinatorialeffect of mutations across multiple disparate genomic regions, includingexpressed and non-expressed genetic elements, and uses gatheredinformation (e.g., experimental results) to predict mutationcombinations expected to produce strain enhancements.

In some embodiments, the present disclosure teaches: i) industrialmicroorganisms, and other host cells amenable to improvement via thedisclosed inventions, ii) generating diversity pools for downstreamanalysis, iii) methods and hardware for high-throughput screening andsequencing of large variant pools, iv) methods and hardware for machinelearning computational analysis and prediction of synergistic effects ofgenome-wide mutations, and v) methods for high-throughput strainengineering.

The following molecular tools and libraries are discussed in terms ofillustrative microbial examples. Persons having skill in the art willrecognize that the HTP molecular tools of the present disclosure arecompatible with any host cell, including eukaryotic cellular, and higherlife forms.

Each of the identified HTP molecular tool sets—which enable the creationof the various HTP genetic design libraries utilized in the microbialengineering platform—will now be discussed.

1. Promoter Swaps: A Molecular Tool for the Derivation of Promoter SwapMicrobial Strain Libraries

In some embodiments, the present disclosure teaches methods of selectingpromoters with optimal expression properties to produce beneficialeffects on overall-host strain phenotype (e.g., yield or productivity).

For example, in some embodiments, the present disclosure teaches methodsof identifying one or more promoters and/or generating variants of oneor more promoters within a host cell, which exhibit a range ofexpression strengths (e.g. promoter ladders discussed infra), orsuperior regulatory properties (e.g., tighter regulatory control forselected genes). A particular combination of these identified and/orgenerated promoters can be grouped together as a promoter ladder, whichis explained in more detail below.

The promoter ladder in question is then associated with a given gene ofinterest. Thus, if one has promoters P₁-P₈ (representing eight promotersthat have been identified and/or generated to exhibit a range ofexpression strengths) and associates the promoter ladder with a singlegene of interest in a microbe (i.e. genetically engineer a microbe witha given promoter operably linked to a given target gene), then theeffect of each combination of the eight promoters can be ascertained bycharacterizing each of the engineered strains resulting from eachcombinatorial effort, given that the engineered microbes have anotherwise identical genetic background except the particular promoter(s)associated with the target gene.

The resultant microbes that are engineered via this process form HTPgenetic design libraries.

The HTP genetic design library can refer to the actual physicalmicrobial strain collection that is formed via this process, with eachmember strain being representative of a given promoter operably linkedto a particular target gene, in an otherwise identical geneticbackground, said library being termed a “promoter swap microbial strainlibrary.”

Furthermore, the HTP genetic design library can refer to the collectionof genetic perturbations—in this case a given promoter x operably linkedto a given gene y—said collection being termed a “promoter swaplibrary.”

Further, one can utilize the same promoter ladder comprising promotersP₁-P₈ to engineer microbes, wherein each of the 8 promoters is operablylinked to 10 different gene targets. The result of this procedure wouldbe 80 microbes that are otherwise assumed genetically identical, exceptfor the particular promoters operably linked to a target gene ofinterest. These 80 microbes could be appropriately screened andcharacterized and give rise to another HTP genetic design library. Thecharacterization of the microbial strains in the HTP genetic designlibrary produces information and data that can be stored in any datastorage construct, including a relational database, an object-orienteddatabase or a highly distributed NoSQL database. This data/informationcould be, for example, a given promoter's (e.g. P₁-P₈) effect whenoperably linked to a given gene target. This data/information can alsobe the broader set of combinatorial effects that result from operablylinking two or more of promoters P₁-P₈ to a given gene target.

The aforementioned examples of eight promoters and 10 target genes ismerely illustrative, as the concept can be applied with any given numberof promoters that have been grouped together based upon exhibition of arange of expression strengths and any given number of target genes.Persons having skill in the art will also recognize the ability tooperably link two or more promoters in front of any gene target. Thus,in some embodiments, the present disclosure teaches promoter swaplibraries in which 1, 2, 3 or more promoters from a promoter ladder areoperably linked to one or more genes.

In summary, utilizing various promoters to drive expression of variousgenes in an organism is a powerful tool to optimize a trait of interest.The molecular tool of promoter swapping, developed by the inventors,uses a ladder of promoter sequences that have been demonstrated to varyexpression of at least one locus under at least one condition. Thisladder is then systematically applied to a group of genes in theorganism using high-throughput genome engineering. This group of genesis determined to have a high likelihood of impacting the trait ofinterest based on any one of a number of methods. These could includeselection based on known function, or impact on the trait of interest,or algorithmic selection based on previously determined beneficialgenetic diversity. In some embodiments, the selection of genes caninclude all the genes in a given host. In other embodiments, theselection of genes can be a subset of all genes in a given host, chosenrandomly.

The resultant HTP genetic design microbial strain library of organismscontaining a promoter sequence linked to a gene is then assessed forperformance in a high-throughput screening model, and promoter-genelinkages which lead to increased performance are determined and theinformation stored in a database. The collection of geneticperturbations (i.e. given promoter x operably linked to a given gene y)form a “promoter swap library,” which can be utilized as a source ofpotential genetic alterations to be utilized in microbial engineeringprocessing. Over time, as a greater set of genetic perturbations isimplemented against a greater diversity of host cell backgrounds, eachlibrary becomes more powerful as a corpus of experimentally confirmeddata that can be used to more precisely and predictably design targetedchanges against any background of interest.

Transcription levels of genes in an organism are a key point of controlfor affecting organism behavior. Transcription is tightly coupled totranslation (protein expression), and which proteins are expressed inwhat quantities determines organism behavior. Cells express thousands ofdifferent types of proteins, and these proteins interact in numerouscomplex ways to create function. By varying the expression levels of aset of proteins systematically, function can be altered in ways that,because of complexity, are difficult to predict. Some alterations mayincrease performance, and so, coupled to a mechanism for assessingperformance, this technique allows for the generation of organisms withimproved function.

In the context of a small molecule synthesis pathway, enzymes interactthrough their small molecule substrates and products in a linear orbranched chain, starting with a substrate and ending with a smallmolecule of interest. Because these interactions are sequentiallylinked, this system exhibits distributed control, and increasing theexpression of one enzyme can only increase pathway flux until anotherenzyme becomes rate limiting.

Metabolic Control Analysis (MCA) is a method for determining, fromexperimental data and first principles, which enzyme or enzymes are ratelimiting. MCA is limited however, because it requires extensiveexperimentation after each expression level change to determine the newrate limiting enzyme. Promoter swapping is advantageous in this context,because through the application of a promoter ladder to each enzyme in apathway, the limiting enzyme is found, and the same thing can be done insubsequent rounds to find new enzymes that become rate limiting.Further, because the read-out on function is better production of thesmall molecule of interest, the experiment to determine which enzyme islimiting is the same as the engineering to increase production, thusshortening development time. In some embodiments the present disclosureteaches the application of PRO swap to genes encoding individualsubunits of multi-unit enzymes. In yet other embodiments, the presentdisclosure teaches methods of applying PRO swap techniques to genesresponsible for regulating individual enzymes, or whole biosyntheticpathways.

In some embodiments, the promoter swap tool of the present disclosurecan is used to identify optimum expression of a selected gene target. Insome embodiments, the goal of the promoter swap may be to increaseexpression of a target gene to reduce bottlenecks in a metabolic orgenetic pathway. In other embodiments, the goal of the promoter swap maybe to reduce the expression of the target gene to avoid unnecessaryenergy expenditures in the host cell, when expression of said targetgene is not required.

In the context of other cellular systems like transcription, transport,or signaling, various rational methods can be used to try and find out,a priori, which proteins are targets for expression change and what thatchange should be. These rational methods reduce the number ofperturbations that must be tested to find one that improves performance,but they do so at significant cost. Gene deletion studies identifyproteins whose presence is critical for a particular function, andimportant genes can then be over-expressed. Due to the complexity ofprotein interactions, this is often ineffective at increasingperformance. Different types of models have been developed that attemptto describe, from first principles, transcription or signaling behavioras a function of protein levels in the cell. These models often suggesttargets where expression changes might lead to different or improvedfunction. The assumptions that underlie these models are simplistic andthe parameters difficult to measure, so the predictions they make areoften incorrect, especially for non-model organisms. With both genedeletion and modeling, the experiments required to determine how toaffect a certain gene are different than the subsequent work to make thechange that improves performance. Promoter swapping sidesteps thesechallenges, because the constructed strain that highlights theimportance of a particular perturbation is also, already, the improvedstrain.

Thus, in particular embodiments, promoter swapping is a multi-stepprocess comprising:

1. Selecting a set of “x” promoters to act as a “ladder.” Ideally thesepromoters have been shown to lead to highly variable expression acrossmultiple genomic loci, but the only requirement is that they perturbgene expression in some way.

2. Selecting a set of “n” genes to target. This set can be every openreading frame (ORF) in a genome, or a subset of ORFs. The subset can bechosen using annotations on ORFs related to function, by relation topreviously demonstrated beneficial perturbations (previous promoterswaps or previous SNP swaps), by algorithmic selection based onepistatic interactions between previously generated perturbations, otherselection criteria based on hypotheses regarding beneficial ORF totarget, or through random selection. In other embodiments, the “n”targeted genes can comprise non-protein coding genes, includingnon-coding RNAs.

3. High-throughput strain engineering to rapidly- and in someembodiments, in parallel-carry out the following genetic modifications:When a native promoter exists in front of target gene n and its sequenceis known, replace the native promoter with each of the x promoters inthe ladder. When the native promoter does not exist, or its sequence isunknown, insert each of the x promoters in the ladder in front of gene n(see e.g., FIG. 21). In this way a “library” (also referred to as a HTPgenetic design library) of strains is constructed, wherein each memberof the library is an instance of x promoter operably linked to n target,in an otherwise identical genetic context. As previously describedcombinations of promoters can be inserted, extending the range ofcombinatorial possibilities upon which the library is constructed.

4. High-throughput screening of the library of strains in a contextwhere their performance against one or more metrics is indicative of theperformance that is being optimized.

This foundational process can be extended to provide furtherimprovements in strain performance by, inter alia: (1) Consolidatingmultiple beneficial perturbations into a single strain background,either one at a time in an interactive process, or as multiple changesin a single step. Multiple perturbations can be either a specific set ofdefined changes or a partly randomized, combinatorial library ofchanges. For example, if the set of targets is every gene in a pathway,then sequential regeneration of the library of perturbations into animproved member or members of the previous library of strains canoptimize the expression level of each gene in a pathway regardless ofwhich genes are rate limiting at any given iteration; (2) Feeding theperformance data resulting from the individual and combinatorialgeneration of the library into an algorithm that uses that data topredict an optimum set of perturbations based on the interaction of eachperturbation; and (3) Implementing a combination of the above twoapproaches (see FIG. 20).

The molecular tool, or technique, discussed above is characterized aspromoter swapping, but is not limited to promoters and can include othersequence changes that systematically vary the expression level of a setof targets. Other methods for varying the expression level of a set ofgenes could include: a) a ladder of ribosome binding sites (or Kozaksequences in eukaryotes); b) replacing the start codon of each targetwith each of the other start codons (i.e start/stop codon exchangesdiscussed infra); c) attachment of various mRNA stabilizing ordestabilizing sequences to the 5′ or 3′ end, or at any other location,of a transcript, d) attachment of various protein stabilizing ordestabilizing sequences at any location in the protein.

The approach is exemplified in the present disclosure with industrialmicroorganisms, but is applicable to any organism where desired traitscan be identified in a population of genetic mutants. For example, thiscould be used for improving the performance of CHO cells, yeast, insectcells, algae, as well as multi-cellular organisms, such as plants.

2. SNP Swaps: A Molecular Tool for the Derivation of SNP Swap MicrobialStrain Libraries

In certain embodiments, SNP swapping is not a random mutagenic approachto improving a microbial strain, but rather involves the systematicintroduction or removal of individual Small Nuclear Polymorphismnucleotide mutations (i.e. SNPs) (hence the name “SNP swapping”) acrossstrains.

The resultant microbes that are engineered via this process form HTPgenetic design libraries.

The HTP genetic design library can refer to the actual physicalmicrobial strain collection that is formed via this process, with eachmember strain being representative of the presence or absence of a givenSNP, in an otherwise identical genetic background, said library beingtermed a “SNP swap microbial strain library.”

Furthermore, the HTP genetic design library can refer to the collectionof genetic perturbations—in this case a given SNP being present or agiven SNP being absent—said collection being termed a “SNP swaplibrary.”

In some embodiments, SNP swapping involves the reconstruction of hostorganisms with optimal combinations of target SNP “building blocks” withidentified beneficial performance effects. Thus, in some embodiments,SNP swapping involves consolidating multiple beneficial mutations into asingle strain background, either one at a time in an iterative process,or as multiple changes in a single step. Multiple changes can be eithera specific set of defined changes or a partly randomized, combinatoriallibrary of mutations.

In other embodiments, SNP swapping also involves removing multiplemutations identified as detrimental from a strain, either one at a timein an iterative process, or as multiple changes in a single step.Multiple changes can be either a specific set of defined changes or apartly randomized, combinatorial library of mutations. In someembodiments, the SNP swapping methods of the present disclosure includeboth the addition of beneficial SNPs, and removing detrimental and/orneutral mutations.

SNP swapping is a powerful tool to identify and exploit both beneficialand detrimental mutations in a lineage of strains subjected tomutagenesis and selection for an improved trait of interest. SNPswapping utilizes high-throughput genome engineering techniques tosystematically determine the influence of individual mutations in amutagenic lineage. Genome sequences are determined for strains acrossone or more generations of a mutagenic lineage with known performanceimprovements. High-throughput genome engineering is then usedsystematically to recapitulate mutations from improved strains inearlier lineage strains, and/or revert mutations in later strains toearlier strain sequences. The performance of these strains is thenevaluated and the contribution of each individual mutation on theimproved phenotype of interest can be determined. As aforementioned, themicrobial strains that result from this process areanalyzed/characterized and form the basis for the SNP swap geneticdesign libraries that can inform microbial strain improvement acrosshost strains.

Removal of detrimental mutations can provide immediate performanceimprovements, and consolidation of beneficial mutations in a strainbackground not subject to mutagenic burden can rapidly and greatlyimprove strain performance. The various microbial strains produced viathe SNP swapping process form the HTP genetic design SNP swappinglibraries, which are microbial strains comprising the variousadded/deleted/or consolidated SNPs, but with otherwise identical geneticbackgrounds.

As discussed previously, random mutagenesis and subsequent screening forperformance improvements is a commonly used technique for industrialstrain improvement, and many strains currently used for large scalemanufacturing have been developed using this process iteratively over aperiod of many years, sometimes decades. Random approaches to generatinggenomic mutations such as exposure to UV radiation or chemical mutagenssuch as ethyl methanesulfonate were a preferred method for industrialstrain improvements because: 1) industrial organisms may be poorlycharacterized genetically or metabolically, rendering target selectionfor directed improvement approaches difficult or impossible; 2) even inrelatively well characterized systems, changes that result in industrialperformance improvements are difficult to predict and may requireperturbation of genes that have no known function, and 3) genetic toolsfor making directed genomic mutations in a given industrial organism maynot be available or very slow and/or difficult to use.

However, despite the aforementioned benefits of this process, there arealso a number of known disadvantages. Beneficial mutations arerelatively rare events, and in order to find these mutations with afixed screening capacity, mutations rates must be sufficiently high.This often results in unwanted neutral and partly detrimental mutationsbeing incorporated into strains along with beneficial changes. Over timethis ‘mutagenic burden’ builds up, resulting in strains withdeficiencies in overall robustness and key traits such as growth rates.Eventually ‘mutagenic burden’ renders further improvements inperformance through random mutagenesis increasingly difficult orimpossible to obtain. Without suitable tools, it is impossible toconsolidate beneficial mutations found in discrete and parallel branchesof strain lineages.

SNP swapping is an approach to overcome these limitations bysystematically recapitulating or reverting some or all mutationsobserved when comparing strains within a mutagenic lineage. In this way,both beneficial (‘causative’) mutations can be identified andconsolidated, and/or detrimental mutations can be identified andremoved. This allows rapid improvements in strain performance that couldnot be achieved by further random mutagenesis or targeted geneticengineering.

Removal of genetic burden or consolidation of beneficial changes into astrain with no genetic burden also provides a new, robust starting pointfor additional random mutagenesis that may enable further improvements.

In addition, as orthogonal beneficial changes are identified acrossvarious, discrete branches of a mutagenic strain lineage, they can berapidly consolidated into better performing strains. These mutations canalso be consolidated into strains that are not part of mutageniclineages, such as strains with improvements gained by directed geneticengineering.

Other approaches and technologies exist to randomly recombine mutationsbetween strains within a mutagenic lineage. These include techniquessuch as protoplast fusion and whole genome shuffling that facilitategenomic recombination across mutated strains. For some industrialmicroorganisms such as yeast and filamentous fungi, natural matingcycles can also be exploited for pairwise genomic recombination. In thisway, detrimental mutations can be removed by ‘back-crossing’ mutantswith parental strains and beneficial mutations consolidated. However,these approaches are subject to many limitations that are circumventedusing the SNP swapping methods of the present disclosure.

For example, as these approaches rely on a relatively small number ofrandom recombination crossover events to swap mutations, it may takemany cycles of recombination and screening to optimize strainperformance. In addition, although natural recombination events areessentially random, they are also subject to genome positional bias andsome mutations may be difficult to address. These approaches alsoprovide little information about the influence of individual mutationswithout additional genome sequencing and analysis. SNP swappingovercomes these fundamental limitations as it is not a random approach,but rather the systematic introduction or removal of individualmutations across strains.

In some embodiments, the present disclosure teaches methods foridentifying the SNP sequence diversity present among the organisms of adiversity pool. A diversity pool can be a given number n of microbesutilized for analysis, with said microbes' genomes representing the“diversity pool.”

In particular aspects, a diversity pool may be an original parent strain(S₁) with a “baseline” or “reference” genetic sequence at a particulartime point (S₁Gen₁) and then any number of subsequent offspring strains(S_(2-n)) that were derived/developed from said S₁ strain and that havea different genome (S_(2-n)Gen_(2-n)), in relation to the baselinegenome of S₁.

For example, in some embodiments, the present disclosure teachessequencing the microbial genomes in a diversity pool to identify theSNPs present in each strain. In one embodiment, the strains of thediversity pool are historical microbial production strains. Thus, adiversity pool of the present disclosure can include for example, anindustrial reference strain, and one or more mutated industrial strainsproduced via traditional strain improvement programs.

In some embodiments, the SNPs within a diversity pool are determinedwith reference to a “reference strain.” In some embodiments, thereference strain is a wild-type strain. In other embodiments, thereference strain is an original industrial strain prior to beingsubjected to any mutagenesis. The reference strain can be defined by thepractitioner and does not have to be an original wild-type strain ororiginal industrial strain. The base strain is merely representative ofwhat will be considered the “base,” “reference” or original geneticbackground, by which subsequent strains that were derived, or weredeveloped from said reference strain, are to be compared.

Once all SNPS in the diversity pool are identified, the presentdisclosure teaches methods of SNP swapping and screening methods todelineate (i.e. quantify and characterize) the effects (e.g. creation ofa phenotype of interest) of SNPs individually and/or in groups.

In some embodiments, the SNP swapping methods of the present disclosurecomprise the step of introducing one or more SNPs identified in amutated strain (e.g., a strain from amongst S_(2-n)Gen_(2-n)) to areference strain (S₁Gen₁) or wild-type strain (“wave up”).

In other embodiments, the SNP swapping methods of the present disclosurecomprise the step of removing one or more SNPs identified in a mutatedstrain (e.g., a strain from amongst S_(2-n)Ge_(2-n)) (“wave down”).

In some embodiments, each generated strain comprising one or more SNPchanges (either introducing or removing) is cultured and analyzed underone or more criteria of the present disclosure (e.g., production of achemical or product of interest). Data from each of the analyzed hoststrains is associated, or correlated, with the particular SNP, or groupof SNPs present in the host strain, and is recorded for future use.Thus, the present disclosure enables the creation of large and highlyannotated HTP genetic design microbial strain libraries that are able toidentify the effect of a given SNP on any number of microbial genetic orphenotypic traits of interest. The information stored in these HTPgenetic design libraries informs the machine learning algorithms of theHTP genomic engineering platform and directs future iterations of theprocess, which ultimately leads to evolved microbial organisms thatpossess highly desirable properties/traits.

3. Start/Stop Codon Exchanges: A Molecular Tool for the Derivation ofStart/Stop Codon Microbial Strain Libraries

In some embodiments, the present disclosure teaches methods of swappingstart and stop codon variants. For example, typical stop codons for S.cerevisiae and mammals are TAA (UAA) and TGA (UGA), respectively. Thetypical stop codon for monocotyledonous plants is TGA (UGA), whereasinsects and E. coli commonly use TAA (UAA) as the stop codon (Dalphin etal. (1996) Nucl. Acids Res. 24: 216-218). In other embodiments, thepresent disclosure teaches use of the TAG (UAG) stop codons.

The present disclosure similarly teaches swapping start codons. In someembodiments, the present disclosure teaches use of the ATG (AUG) startcodon utilized by most organisms (especially eukaryotes). In someembodiments, the present disclosure teaches that prokaryotes use ATG(AUG) the most, followed by GTG (GUG) and TTG (UUG).

In other embodiments, the present invention teaches replacing ATG startcodons with TTG. In some embodiments, the present invention teachesreplacing ATG start codons with GTG. In some embodiments, the presentinvention teaches replacing GTG start codons with ATG. In someembodiments, the present invention teaches replacing GTG start codonswith TTG. In some embodiments, the present invention teaches replacingTTG start codons with ATG. In some embodiments, the present inventionteaches replacing TTG start codons with GTG.

In other embodiments, the present invention teaches replacing TAA stopcodons with TAG. In some embodiments, the present invention teachesreplacing TAA stop codons with TGA. In some embodiments, the presentinvention teaches replacing TGA stop codons with TAA. In someembodiments, the present invention teaches replacing TGA stop codonswith TAG. In some embodiments, the present invention teaches replacingTAG stop codons with TAA. In some embodiments, the present inventionteaches replacing TAG stop codons with TGA.

4. Stop Swap: A Molecular Tool for the Derivation of Optimized SequenceMicrobial Strain Libraries

In some embodiments, the present disclosure teaches methods of improvinghost cell productivity through the optimization of cellular genetranscription. Gene transcription is the result of several distinctbiological phenomena, including transcriptional initiation (RNAprecruitment and transcriptional complex formation), elongation (strandsynthesis/extension), and transcriptional termination (RNAp detachmentand termination). Although much attention has been devoted to thecontrol of gene expression through the transcriptional modulation ofgenes (e.g., by changing promoters, or inducing regulatory transcriptionfactors), comparatively few efforts have been made towards themodulation of transcription via the modulation of gene terminatorsequences.

The most obvious way that transcription impacts on gene expressionlevels is through the rate of Pol II initiation, which can be modulatedby combinations of promoter or enhancer strength and trans-activatingfactors (Kadonaga, J T. 2004 “Regulation of RNA polymerase IItranscription by sequence-specific DNA binding factors” Cell. 2004 Jan.23; 116(2):247-57). In eukaryotes, elongation rate may also determinegene expression patterns by influencing alternative splicing (Cramer P.et al., 1997 “Functional association between promoter structure andtranscript alternative splicing.” Proc Natl Acad Sci USA. 1997 Oct. 14;94(21):11456-60). Failed termination on a gene can impair the expressionof downstream genes by reducing the accessibility of the promoter to PolII (Greger I H. et al., 2000 “Balancing transcriptional interference andinitiation on the GALT promoter of Saccharomyces cerevisiae.” Proc NatlAcad Sci USA. 2000 Jul. 18; 97(15):8415-20). This process, known astranscriptional interference, is particularly relevant in lowereukaryotes, as they often have closely spaced genes.

Termination sequences can also affect the expression of the genes towhich the sequences belong. For example, studies show that inefficienttranscriptional termination in eukaryotes results in an accumulation ofunspliced pre-mRNA (see West, S., and Proudfoot, N.J., 2009“Transcriptional Termination Enhances Protein Expression in Human Cells”Mol Cell. 2009 Feb. 13; 33(3-9); 354-364). Other studies have also shownthat 3′ end processing, can be delayed by inefficient termination (West,S et al., 2008 “Molecular dissection of mammalian RNA polymerase IItranscriptional termination.” Mol Cell. 2008 Mar. 14; 29(5):600-10.).Transcriptional termination can also affect mRNA stability by releasingtranscripts from sites of synthesis.

Termination of Transcription Mechanism in Eukaryotes

Transcriptional termination in eukaryotes operates through terminatorsignals that are recognized by protein factors associated with the RNApolymerase II. In some embodiments, the cleavage and polyadenylationspecificity factor (CPSF) and cleavage stimulation factor (CstF)transfer from the carboxyl terminal domain of RNA polymerase II to thepoly-A signal. In some embodiments, the CPSF and CstF factors alsorecruit other proteins to the termination site, which then cleave thetranscript and free the mRNA from the transcription complex. Terminationalso triggers polyadenylation of mRNA transcripts. Illustrative examplesof validated eukaryotic termination factors, and their conservedstructures are discussed in later portions of this document.

Termination of Transcription in Prokaryotes

In prokaryotes, two principal mechanisms, termed Rho-independent andRho-dependent termination, mediate transcriptional termination.Rho-independent termination signals do not require an extrinsictranscription-termination factor, as formation of a stem-loop structurein the RNA transcribed from these sequences along with a series ofUridine (U) residues promotes release of the RNA chain from thetranscription complex. Rho-dependent termination, on the other hand,requires a transcription-termination factor called Rho and cis-actingelements on the mRNA. The initial binding site for Rho, the Rhoutilization (rut) site, is an extended C70 nucleotides, sometimes 80-100nucleotides) single-stranded region characterized by a high cytidine/lowguanosine content and relatively little secondary structure in the RNAbeing synthesized, upstream of the actual terminator sequence. When apolymerase pause site is encountered, termination occurs, and thetranscript is released by Rho's helicase activity.

Terminator Swapping (STOP Swap)

In some embodiments, the present disclosure teaches methods of selectingtermination sequences (“terminators”) with optimal expression propertiesto produce beneficial effects on overall-host strain productivity.

For example, in some embodiments, the present disclosure teaches methodsof identifying one or more terminators and/or generating variants of oneor more terminators within a host cell, which exhibit a range ofexpression strengths (e.g. terminator ladders discussed infra). Aparticular combination of these identified and/or generated terminatorscan be grouped together as a terminator ladder, which is explained inmore detail below.

The terminator ladder in question is then associated with a given geneof interest. Thus, if one has terminators T₁-T₈ (representing eightterminators that have been identified and/or generated to exhibit arange of expression strengths when combined with one or more promoters)and associates the terminator ladder with a single gene of interest in ahost cell (i.e. genetically engineer a host cell with a given terminatoroperably linked to the 3′ end of to a given target gene), then theeffect of each combination of the terminators can be ascertained bycharacterizing each of the engineered strains resulting from eachcombinatorial effort, given that the engineered host cells have anotherwise identical genetic background except the particular promoter(s)associated with the target gene. The resultant host cells that areengineered via this process form HTP genetic design libraries.

The HTP genetic design library can refer to the actual physicalmicrobial strain collection that is formed via this process, with eachmember strain being representative of a given terminator operably linkedto a particular target gene, in an otherwise identical geneticbackground, said library being termed a “terminator swap microbialstrain library” or “STOP swap microbial strain library.”

Furthermore, the HTP genetic design library can refer to the collectionof genetic perturbations—in this case a given terminator x operablylinked to a given gene y—said collection being termed a “terminator swaplibrary” or “STOP swap library.”

Further, one can utilize the same terminator ladder comprising promotersT₁-T₈ to engineer microbes, wherein each of the eight terminators isoperably linked to 10 different gene targets. The result of thisprocedure would be 80 host cell strains that are otherwise assumedgenetically identical, except for the particular terminators operablylinked to a target gene of interest. These 80 host cell strains could beappropriately screened and characterized and give rise to another HTPgenetic design library. The characterization of the microbial strains inthe HTP genetic design library produces information and data that can bestored in any database, including without limitation, a relationaldatabase, an object-oriented database or a highly distributed NoSQLdatabase. This data/information could include, for example, a giventerminators' (e.g., T₁-T₈) effect when operably linked to a given genetarget. This data/information can also be the broader set ofcombinatorial effects that result from operably linking two or more ofpromoters T₁-T₈ to a given gene target.

The aforementioned examples of eight terminators and 10 target genes ismerely illustrative, as the concept can be applied with any given numberof promoters that have been grouped together based upon exhibition of arange of expression strengths and any given number of target genes.

In summary, utilizing various terminators to modulate expression ofvarious genes in an organism is a powerful tool to optimize a trait ofinterest. The molecular tool of terminator swapping, developed by theinventors, uses a ladder of terminator sequences that have beendemonstrated to vary expression of at least one locus under at least onecondition. This ladder is then systematically applied to a group ofgenes in the organism using high-throughput genome engineering. Thisgroup of genes is determined to have a high likelihood of impacting thetrait of interest based on any one of a number of methods. These couldinclude selection based on known function, or impact on the trait ofinterest, or algorithmic selection based on previously determinedbeneficial genetic diversity.

The resultant HTP genetic design microbial library of organismscontaining a terminator sequence linked to a gene is then assessed forperformance in a high-throughput screening model, and promoter-genelinkages which lead to increased performance are determined and theinformation stored in a database. The collection of geneticperturbations (i.e. given terminator x linked to a given gene y) form a“terminator swap library,” which can be utilized as a source ofpotential genetic alterations to be utilized in microbial engineeringprocessing. Over time, as a greater set of genetic perturbations isimplemented against a greater diversity of microbial backgrounds, eachlibrary becomes more powerful as a corpus of experimentally confirmeddata that can be used to more precisely and predictably design targetedchanges against any background of interest. That is in some embodiments,the present disclosures teaches introduction of one or more geneticchanges into a host cell based on previous experimental results embeddedwithin the meta data associated with any of the genetic design librariesof the invention.

Thus, in particular embodiments, terminator swapping is a multi-stepprocess comprising:

1. Selecting a set of “x” terminators to act as a “ladder.” Ideallythese terminators have been shown to lead to highly variable expressionacross multiple genomic loci, but the only requirement is that theyperturb gene expression in some way.

2. Selecting a set of “n” genes to target. This set can be every ORF ina genome, or a subset of ORFs. The subset can be chosen usingannotations on ORFs related to function, by relation to previouslydemonstrated beneficial perturbations (previous promoter swaps, STOPswaps, or SNP swaps), by algorithmic selection based on epistaticinteractions between previously generated perturbations, other selectioncriteria based on hypotheses regarding beneficial ORF to target, orthrough random selection. In other embodiments, the “n” targeted genescan comprise non-protein coding genes, including non-coding RNAs.

3. High-throughput strain engineering to rapidly and in parallel carryout the following genetic modifications: When a native terminator existsat the 3′ end of target gene n and its sequence is known, replace thenative terminator with each of the x terminators in the ladder. When thenative terminator does not exist, or its sequence is unknown, inserteach of the x terminators in the ladder after the gene stop codon.

In this way a “library” (also referred to as a HTP genetic designlibrary) of strains is constructed, wherein each member of the libraryis an instance of x terminator linked to n target, in an otherwiseidentical genetic context. As previously described, combinations ofterminators can be inserted, extending the range of combinatorialpossibilities upon which the library is constructed.

4. High-throughput screening of the library of strains in a contextwhere their performance against one or more metrics is indicative of theperformance that is being optimized.

This foundational process can be extended to provide furtherimprovements in strain performance by, inter alia: (1) Consolidatingmultiple beneficial perturbations into a single strain background,either one at a time in an interactive process, or as multiple changesin a single step. Multiple perturbations can be either a specific set ofdefined changes or a partly randomized, combinatorial library ofchanges. For example, if the set of targets is every gene in a pathway,then sequential regeneration of the library of perturbations into animproved member or members of the previous library of strains canoptimize the expression level of each gene in a pathway regardless ofwhich genes are rate limiting at any given iteration; (2) Feeding theperformance data resulting from the individual and combinatorialgeneration of the library into an algorithm that uses that data topredict an optimum set of perturbations based on the interaction of eachperturbation; and (3) Implementing a combination of the above twoapproaches.

The approach is exemplified in the present disclosure with industrialmicroorganisms, but is applicable to any organism where desired traitscan be identified in a population of genetic mutants. For example, thiscould be used for improving the performance of CHO cells, yeast, insectcells, algae, as well as multi-cellular organisms, such as plants.

5. Sequence Optimization: A Molecular Tool for the Derivation ofOptimized Sequence Microbial Strain Libraries

In one embodiment, the methods of the provided disclosure comprise codonoptimizing one or more genes expressed by the host organism. Methods foroptimizing codons to improve expression in various hosts are known inthe art and are described in the literature (see U.S. Pat. App. Pub. No.2007/0292918, incorporated herein by reference in its entirety).Optimized coding sequences containing codons preferred by a particularprokaryotic or eukaryotic host (see also, Murray et al. (1989) Nucl.Acids Res. 17:477-508) can be prepared, for example, to increase therate of translation or to produce recombinant RNA transcripts havingdesirable properties, such as a longer half-life, as compared withtranscripts produced from a non-optimized sequence.

Protein expression is governed by a host of factors including those thataffect transcription, mRNA processing, and stability and initiation oftranslation. Optimization can thus address any of a number of sequencefeatures of any particular gene. As a specific example, a rare codoninduced translational pause can result in reduced protein expression. Arare codon induced translational pause includes the presence of codonsin the polynucleotide of interest that are rarely used in the hostorganism may have a negative effect on protein translation due to theirscarcity in the available tRNA pool.

Alternate translational initiation also can result in reducedheterologous protein expression. Alternate translational initiation caninclude a synthetic polynucleotide sequence inadvertently containingmotifs capable of functioning as a ribosome binding site (RBS). Thesesites can result in initiating translation of a truncated protein from agene-internal site. One method of reducing the possibility of producinga truncated protein, which can be difficult to remove duringpurification, includes eliminating putative internal RBS sequences froman optimized polynucleotide sequence.

Repeat-induced polymerase slippage can result in reduced heterologousprotein expression. Repeat-induced polymerase slippage involvesnucleotide sequence repeats that have been shown to cause slippage orstuttering of DNA polymerase which can result in frameshift mutations.Such repeats can also cause slippage of RNA polymerase. In an organismwith a high G+C content bias, there can be a higher degree of repeatscomposed of G or C nucleotide repeats. Therefore, one method of reducingthe possibility of inducing RNA polymerase slippage, includes alteringextended repeats of G or C nucleotides.

Interfering secondary structures also can result in reduced heterologousprotein expression. Secondary structures can sequester the RBS sequenceor initiation codon and have been correlated to a reduction in proteinexpression. Stemloop structures can also be involved in transcriptionalpausing and attenuation. An optimized polynucleotide sequence cancontain minimal secondary structures in the RBS and gene coding regionsof the nucleotide sequence to allow for improved transcription andtranslation.

For example, the optimization process can begin by identifying thedesired amino acid sequence to be expressed by the host. From the aminoacid sequence a candidate polynucleotide or DNA sequence can bedesigned. During the design of the synthetic DNA sequence, the frequencyof codon usage can be compared to the codon usage of the host expressionorganism and rare host codons can be removed from the syntheticsequence. Additionally, the synthetic candidate DNA sequence can bemodified in order to remove undesirable enzyme restriction sites and addor remove any desired signal sequences, linkers or untranslated regions.The synthetic DNA sequence can be analyzed for the presence of secondarystructure that may interfere with the translation process, such as G/Crepeats and stem-loop structures.

6. Epistasis Mapping—a Predictive Analytical Tool Enabling BeneficialGenetic Consolidations

In some embodiments, the present disclosure teaches epistasis mappingmethods for predicting and combining beneficial genetic alterations intoa host cell. The genetic alterations may be created by any of theaforementioned HTP molecular tool sets (e.g., promoter swaps, SNP swaps,start/stop codon exchanges, sequence optimization) and the effect ofthose genetic alterations would be known from the characterization ofthe derived HTP genetic design microbial strain libraries. Thus, as usedherein, the term epistasis mapping includes methods of identifyingcombinations of genetic alterations (e.g., beneficial SNPs or beneficialpromoter/target gene associations) that are likely to yield increases inhost performance.

In embodiments, the epistasis mapping methods of the present disclosureare based on the idea that the combination of beneficial mutations fromtwo different functional groups is more likely to improve hostperformance, as compared to a combination of mutations from the samefunctional group. See, e.g., Costanzo, The Genetic Landscape of a Cell,Science, Vol. 327, Issue 5964, Jan. 22, 2010, pp. 425-431 (incorporatedby reference herein in its entirety).

Mutations from the same functional group are more likely to operate bythe same mechanism, and are thus more likely to exhibit negative orneutral epistasis on overall host performance. In contrast, mutationsfrom different functional groups are more likely to operate byindependent mechanisms, which can lead to improved host performance andin some instances synergistic effects. For example, referring to FIG.19, lysA and zwf are genes that operate in different pathways to achievethe production of lysine. Based upon the dissimilarity in the individualperformance of those genes, genetic changes using those genes shouldresult in additive consolidation effects. This was borne out in theactual measurement of the consolidated effects of the combination oflysA and zwf, as shown in FIG. 16B and Examples 6.

Thus, in some embodiments, the present disclosure teaches methods ofanalyzing SNP mutations to identify SNPs predicted to belong todifferent functional groups. In some embodiments, SNP functional groupsimilarity is determined by computing the cosine similarity of mutationinteraction profiles (similar to a correlation coefficient, see FIG.16A). The present disclosure also illustrates comparing SNPs via amutation similarity matrix (see FIG. 15) or dendrogram (see FIG. 16A).

Thus, the epistasis mapping procedure provides a method for groupingand/or ranking a diversity of genetic mutations applied in one or moregenetic backgrounds for the purposes of efficient and effectiveconsolidations of said mutations into one or more genetic backgrounds.

In aspects, consolidation is performed with the objective of creatingnovel strains which are optimized for the production of targetbiomolecules. Through the taught epistasis mapping procedure, it ispossible to identify functional groupings of mutations, and suchfunctional groupings enable a consolidation strategy that minimizesundesirable epistatic effects.

As previously explained, the optimization of microbes for use inindustrial fermentation is an important and difficult problem, withbroad implications for the economy, society, and the natural world.Traditionally, microbial engineering has been performed through a slowand uncertain process of random mutagenesis. Such approaches leveragethe natural evolutionary capacity of cells to adapt to artificiallyimposed selection pressure. Such approaches are also limited by therarity of beneficial mutations, the ruggedness of the underlying fitnesslandscape, and more generally underutilize the state of the art incellular and molecular biology.

Modern approaches leverage new understanding of cellular function at themechanistic level and new molecular biology tools to perform targetedgenetic manipulations to specific phenotypic ends. In practice, suchrational approaches are confounded by the underlying complexity ofbiology. Causal mechanisms are poorly understood, particularly whenattempting to combine two or more changes that each has an observedbeneficial effect. Sometimes such consolidations of genetic changesyield positive outcomes (measured by increases in desired phenotypicactivity), although the net positive outcome may be lower than expectedand in some cases higher than expected. In other instances, suchcombinations produce either net neutral effect or a net negative effect.This phenomenon is referred to as epistasis, and is one of thefundamental challenges to microbial engineering (and genetic engineeringgenerally).

As aforementioned, the present HTP genomic engineering platform solvesmany of the problems associated with traditional microbial engineeringapproaches. The present HTP platform uses automation technologies toperform hundreds or thousands of genetic mutations at once. Inparticular aspects, unlike the rational approaches described above, thedisclosed HTP platform enables the parallel construction of thousands ofmutants to more effectively explore large subsets of the relevantgenomic space, as disclosed in U.S. application Ser. No. 15/140,296,entitled Microbial Strain Design System And Methods For ImprovedLarge-Scale Production Of Engineered Nucleotide Sequences, incorporatedby reference herein in its entirety. By trying “everything,” the presentHTP platform sidesteps the difficulties induced by our limitedbiological understanding.

However, at the same time, the present HTP platform faces the problem ofbeing fundamentally limited by the combinatorial explosive size ofgenomic space, and the effectiveness of computational techniques tointerpret the generated data sets given the complexity of geneticinteractions. Techniques are needed to explore subsets of vastcombinatorial spaces in ways that maximize non-random selection ofcombinations that yield desired outcomes.

Somewhat similar HTP approaches have proved effective in the case ofenzyme optimization. In this niche problem, a genomic sequence ofinterest (on the order of 1000 bases), encodes a protein chain with somecomplicated physical configuration. The precise configuration isdetermined by the collective electromagnetic interactions between itsconstituent atomic components. This combination of short genomicsequence and physically constrained folding problem lends itselfspecifically to greedy optimization strategies. That is, it is possibleto individually mutate the sequence at every residue and shuffle theresulting mutants to effectively sample local sequence space at aresolution compatible with the Sequence Activity Response modeling.

However, for full genomic optimizations for biomolecules, suchresidue-centric approaches are insufficient for some important reasons.First, because of the exponential increase in relevant sequence spaceassociated with genomic optimizations for biomolecules. Second, becauseof the added complexity of regulation, expression, and metabolicinteractions in biomolecule synthesis. The present inventors have solvedthese problems via the taught epistasis mapping procedure.

The taught method for modeling epistatic interactions, between acollection of mutations for the purposes of more efficient and effectiveconsolidation of said mutations into one or more genetic backgrounds, isgroundbreaking and highly needed in the art.

When describing the epistasis mapping procedure, the terms “moreefficient” and “more effective” refers to the avoidance of undesirableepistatic interactions among consolidation strains with respect toparticular phenotypic objectives.

As the process has been generally elaborated upon above, a more specificworkflow example will now be described.

First, one begins with a library of M mutations and one or more geneticbackgrounds (e.g., parent bacterial strains). Neither the choice oflibrary nor the choice of genetic backgrounds is specific to the methoddescribed here. But in a particular implementation, a library ofmutations may include exclusively, or in combination: SNP swaplibraries, Promoter swap libraries, or any other mutation librarydescribed herein.

In one implementation, only a single genetic background is provided. Inthis case, a collection of distinct genetic backgrounds (microbialmutants) will first be generated from this single background. This maybe achieved by applying the primary library of mutations (or some subsetthereof) to the given background for example, application of a HTPgenetic design library of particular SNPs or a HTP genetic designlibrary of particular promoters to the given genetic background, tocreate a population (perhaps 100's or 1,000's) of microbial mutants withan identical genetic background except for the particular geneticalteration from the given HTP genetic design library incorporatedtherein. As detailed below, this embodiment can lead to a combinatoriallibrary or pairwise library.

In another implementation, a collection of distinct known geneticbackgrounds may simply be given. As detailed below, this embodiment canlead to a subset of a combinatorial library.

In a particular implementation, the number of genetic backgrounds andgenetic diversity between these backgrounds (measured in number ofmutations or sequence edit distance or the like) is determined tomaximize the effectiveness of this method.

A genetic background may be a natural, native or wild-type strain or amutated, engineered strain. N distinct background strains may berepresented by a vector b. In one example, the background b mayrepresent engineered backgrounds formed by applying N primary mutationsm₀=(m₁, m₂, . . . m_(N)) to a wild-type background strain b₀ to form theN mutated background strains b=m₀ b₀=(m₁b₀, m₂b₀, . . . m_(N) b₀), wherem_(i)b₀ represents the application of mutation m_(i) to backgroundstrain b₀.

In either case (i.e. a single provided genetic background or acollection of genetic backgrounds), the result is a collection of Ngenetically distinct backgrounds. Relevant phenotypes are measured foreach background.

Second, each mutation in a collection of M mutations m₁ is applied toeach background within the collection of N background strains b to forma collection of M×N mutants. In the implementation where the Nbackgrounds were themselves obtained by applying the primary set ofmutations m₀ (as described above), the resulting set of mutants willsometimes be referred to as a combinatorial library or a pairwiselibrary. In another implementation, in which a collection of knownbackgrounds has been provided explicitly, the resulting set of mutantsmay be referred to as a subset of a combinatorial library. Similar togeneration of engineered background vectors, in embodiments, the inputinterface 202 receives the mutation vector m₁ and the background vectorb, and a specified operation such as cross product.

Continuing with the engineered background example above, forming the M×Ncombinatorial library may be represented by the matrix formed by m₁×m₀b₀, the cross product of m₁ applied to the N backgrounds of b=m₀ b₀,where each mutation in m₁ is applied to each background strain within b.Each ith row of the resulting M×N matrix represents the application ofthe ith mutation within m₁ to all the strains within backgroundcollection b. In one embodiment, m₁=m₀ and the matrix represents thepairwise application of the same mutations to starting strain b₀. Inthat case, the matrix is symmetric about its diagonal (M=N), and thediagonal may be ignored in any analysis since it represents theapplication of the same mutation twice.

In embodiments, forming the M×N matrix may be achieved by inputting intothe input interface 202 the compound expression m₁×m₀b₀. The componentvectors of the expression may be input directly with their elementsexplicitly specified, via one or more DNA specifications, or as calls tothe library 206 to enable retrieval of the vectors during interpretationby interpreter 204. As described in U.S. patent application Ser. No.15/140,296, entitled “Microbial Strain Design System and Methods forImproved Large Scale Production of Engineered Nucleotide Sequences,” viathe interpreter 204, execution engine 207, order placement engine 208,and factory 210, the LIMS system 200 generates the microbial strainsspecified by the input expression.

Third, with reference to FIG. 42, the analysis equipment 214 measuresphenotypic responses for each mutant within the M×N combinatoriallibrary matrix (4202). As such, the collection of responses can beconstrued as an M×N Response Matrix R. Each element of R may berepresented as r_(ij)=y(m_(i), m_(j)), where y represents the response(performance) of background strain b_(j) within engineered collection bas mutated by mutation m_(i). For simplicity, and practicality, weassume pairwise mutations where m₁=m₀. Where, as here, the set ofmutations represents a pairwise mutation library, the resulting matrixmay also be referred to as a gene interaction matrix or, moreparticularly, as a mutation interaction matrix.

Those skilled in the art will recognize that, in some embodiments,operations related to epistatic effects and predictive strain design maybe performed entirely through automated means of the LIMS system 200,e.g., by the analysis equipment 214, or by human implementation, orthrough a combination of automated and manual means. When an operationis not fully automated, the elements of the LIMS system 200, e.g.,analysis equipment 214, may, for example, receive the results of thehuman performance of the operations rather than generate results throughits own operational capabilities. As described elsewhere herein,components of the LIMS system 200, such as the analysis equipment 214,may be implemented wholly or partially by one or more computer systems.In some embodiments, in particular where operations related topredictive strain design are performed by a combination of automated andmanual means, the analysis equipment 214 may include not only computerhardware, software or firmware (or a combination thereof), but alsoequipment operated by a human operator such as that listed in Table 5below, e.g., the equipment listed under the category of “Evaluateperformance.”

Fourth, the analysis equipment 212 normalizes the response matrix.Normalization consists of a manual and/or, in this embodiment, automatedprocesses of adjusting measured response values for the purpose ofremoving bias and/or isolating the relevant portions of the effectspecific to this method. With respect to FIG. 42, the first step 4202may include obtaining normalized measured data. In general, in theclaims directed to predictive strain design and epistasis mapping, theterms “performance measure” or “measured performance” or the like may beused to describe a metric that reflects measured data, whether raw orprocessed in some manner, e.g., normalized data. In a particularimplementation, normalization may be performed by subtracting apreviously measured background response from the measured responsevalue. In that implementation, the resulting response elements may beformed as r_(ij)=y(m_(i), m_(j))−y(m_(j)), where y(m_(j)) is theresponse of the engineered background strain b_(j) within engineeredcollection b caused by application of primary mutation m_(j) to parentstrain b₀. Note that each row of the normalized response matrix istreated as a response profile for its corresponding mutation. That is,the ith row describes the relative effect of the corresponding mutationm_(i) applied to all the background strains b_(j) for j=1 to N.

With respect to the example of pairwise mutations, the combinedperformance/response of strains resulting from two mutations may begreater than, less than, or equal to the performance/response of thestrain to each of the mutations individually. This effect is known as“epistasis,” and may, in some embodiments, be represented ase_(ij)=y(m_(i), m_(j))+(y(m_(i))+y(m_(j))). Variations of thismathematical representation are possible, and may depend upon, forexample, how the individual changes biologically interact. As notedabove, mutations from the same functional group are more likely tooperate by the same mechanism, and are thus more likely to exhibitnegative or neutral epistasis on overall host performance. In contrast,mutations from different functional groups are more likely to operate byindependent mechanisms, which can lead to improved host performance byreducing redundant mutative effects, for example. Thus, mutations thatyield dissimilar responses are more likely to combine in an additivemanner than mutations that yield similar responses. This leads to thecomputation of similarity in the next step.

Fifth, the analysis equipment 214 measures the similarity among theresponses—in the pairwise mutation example, the similarity between theeffects of the ith mutation and jth (e.g., primary) mutation within theresponse matrix (4204). Recall that the ith row of R represents theperformance effects of the ith mutation m_(i) on the N backgroundstrains, each of which may be itself the result of engineered mutationsas described above. Thus, the similarity between the effects of the ithand jth mutations may be represented by the similarity s_(ij) betweenthe ith and jth rows, ρ_(i) and ρ_(j), respectively, to form asimilarity matrix S, an example of which is illustrated in FIG. 15.Similarity may be measured using many known techniques, such ascross-correlation or absolute cosine similarity, e.g.,s_(ij)=abs(cos(ρ_(i), ρ_(j))).

As an alternative or supplement to a metric like cosine similarity,response profiles may be clustered to determine degree of similarity.Clustering may be performed by use of a distance-based clusteringalgorithms (e.g. k-mean, hierarchical agglomerative, etc.) inconjunction with suitable distance measure (e.g. Euclidean, Hamming,etc). Alternatively, clustering may be performed using similarity basedclustering algorithms (e.g. spectral, min-cut, etc.) with a suitablesimilarity measure (e.g. cosine, correlation, etc). Of course, distancemeasures may be mapped to similarity measures and vice-versa via anynumber of standard functional operations (e.g., the exponentialfunction). In one implementation, hierarchical agglomerative clusteringmay be used in conjunction absolute cosine similarity. (See FIG. 16A).

As an example of clustering, let C be a clustering of mutations m_(i)into k distinct clusters. Let C be the cluster membership matrix, wherec_(ij) is the degree to which mutation i belongs to cluster j, a valuebetween 0 and 1. The cluster-based similarity between mutations i and jis then given by C_(i)×C_(j) (the dot product of the ith and jth rows ofC). In general, the cluster-based similarity matrix is given by CC^(T)(that is, C times C-transpose). In the case of hard-clustering (amutation belongs to exactly one cluster), the similarity between twomutations is 1 if they belong to the same cluster and 0 if not.

As is described in Costanzo, The Genetic Landscape of a Cell, Science,Vol. 327, Issue 5964, Jan. 22, 2010, pp. 425-431 (incorporated byreference herein in its entirety), such a clustering of mutationresponse profiles relates to an approximate mapping of a cell'sunderlying functional organization. That is, mutations that clustertogether tend to be related by an underlying biological process ormetabolic pathway. Such mutations are referred to herein as a“functional group.” The key observation of this method is that if twomutations operate by the same biological process or pathway, thenobserved effects (and notably observed benefits) may be redundant.Conversely, if two mutations operate by distant mechanism, then it isless likely that beneficial effects will be redundant.

Sixth, based on the epistatic effect, the analysis equipment 214 selectspairs of mutations that lead to dissimilar responses, e.g., their cosinesimilarity metric falls below a similarity threshold, or their responsesfall within sufficiently separated clusters, (e.g., in FIG. 15 and FIG.16A) as shown in FIG. 42 (4206). Based on their dissimilarity, theselected pairs of mutations should consolidate into background strainsbetter than similar pairs.

Based upon the selected pairs of mutations that lead to sufficientlydissimilar responses, the LIMS system (e.g., all of or some combinationof interpreter 204, execution engine 207, order placer 208, and factory210) may be used to design microbial strains having those selectedmutations (4208). In embodiments, as described below and elsewhereherein, epistatic effects may be built into, or used in conjunction withthe predictive model to weight or filter strain selection.

It is assumed that it is possible to estimate the performance (a.k.a.score) of a hypothetical strain obtained by consolidating a collectionof mutations from the library into a particular background via somepreferred predictive model. A representative predictive model utilizedin the taught methods is provided in the below section entitled“Predictive Strain Design” that is found in the larger section of:“Computational Analysis and Prediction of Effects of Genome-Wide GeneticDesign Criteria.”

When employing a predictive strain design technique such as linearregression, the analysis equipment 214 may restrict the model tomutations having low similarity measures by, e.g., filtering theregression results to keep only sufficiently dissimilar mutations.Alternatively, the predictive model may be weighted with the similaritymatrix. For example, some embodiments may employ a weighted leastsquares regression using the similarity matrix to characterize theinterdependencies of the proposed mutations. As an example, weightingmay be performed by applying the “kernel” trick to the regression model.(To the extent that the “kernel trick” is general to many machinelearning modeling approaches, this re-weighting strategy is notrestricted to linear regression.)

Such methods are known to one skilled in the art. In embodiments, thekernel is a matrix having elements 1−w*s_(ij) where 1 is an element ofthe identity matrix, and w is a real value between 0 and 1. When w=0,this reduces to a standard regression model. In practice, the value of wwill be tied to the accuracy (r² value or root mean square error (RMSE))of the predictive model when evaluated against the pairwisecombinatorial constructs and their associate effects y(m_(i), m_(j)). Inone simple implementation, w is defined as w=1−r². In this case, whenthe model is fully predictive, w=1−r²=0 and consolidation is basedsolely on the predictive model and epistatic mapping procedure plays norole. On the other hand, when the predictive model is not predictive atall, w=1−r²=1 and consolidation is based solely on the epistatic mappingprocedure. During each iteration, the accuracy can be assessed todetermine whether model performance is improving.

It should be clear that the epistatic mapping procedure described hereindoes not depend on which model is used by the analysis equipment 214.Given such a predictive model, it is possible to score and rank allhypothetical strains accessible to the mutation library viacombinatorial consolidation.

In some embodiments, to account for epistatic effects, the dissimilarmutation response profiles may be used by the analysis equipment 214 toaugment the score and rank associated with each hypothetical strain fromthe predictive model. This procedure may be thought of broadly as are-weighting of scores, so as to favor candidate strains with dissimilarresponse profiles (e.g., strains drawn from a diversity of clusters). Inone simple implementation, a strain may have its score reduced by thenumber of constituent mutations that do not satisfy the dissimilaritythreshold or that are drawn from the same cluster (with suitableweighting). In a particular implementation, a hypothetical strain'sperformance estimate may be reduced by the sum of terms in thesimilarity matrix associated with all pairs of constituent mutationsassociated with the hypothetical strain (again with suitable weighting).Hypothetical strains may be re-ranked using these augmented scores. Inpractice, such re-weighting calculations may be performed in conjunctionwith the initial scoring estimation.

The result is a collection of hypothetical strains with score and rankaugmented to more effectively avoid confounding epistatic interactions.Hypothetical strains may be constructed at this time, or they may bepassed to another computational method for subsequent analysis or use.

Those skilled in the art will recognize that epistasis mapping anditerative predictive strain design as described herein are not limitedto employing only pairwise mutations, but may be expanded to thesimultaneous application of many more mutations to a background strain.In another embodiment, additional mutations may be applied sequentiallyto strains that have already been mutated using mutations selectedaccording to the predictive methods described herein. In anotherembodiment, epistatic effects are imputed by applying the same geneticmutation to a number of strain backgrounds that differ slightly fromeach other, and noting any significant differences in positive responseprofiles among the modified strain backgrounds.

Organisms Amenable to Genetic Design

The disclosed HTP genomic engineering platform is exemplified withindustrial microbial cell cultures (e.g., Corynebacterium and A. niger),but is applicable to any host cell organism where desired traits can beidentified in a population of genetic mutants.

Thus, as used herein, the term “microorganism” should be taken broadly.It includes, but is not limited to, the two prokaryotic domains,Bacteria and Archaea, as well as certain eukaryotic fungi and protists.However, in certain aspects, “higher” eukaryotic organisms such asinsects, plants, and animals can be utilized in the methods taughtherein.

The present disclosure provides working examples for both prokaryotic(Examples 1-9) and eukaryotic (Example 10-11) host cells

Suitable host cells include, but are not limited to: bacterial cells,algal cells, plant cells, fungal cells, insect cells, and mammaliancells. In one illustrative embodiment, suitable host cells include E.coli (e.g., SHuffle™ competent E. coli available from New EnglandBioLabs in Ipswich, Mass.).

Other suitable host organisms of the present disclosure includemicroorganisms of the genus Corynebacterium. In some embodiments,preferred Corynebacterium strains/species include: C. efficiens, withthe deposited type strain being DSM44549, C. glutamicum, with thedeposited type strain being ATCC13032, and C. ammoniagenes, with thedeposited type strain being ATCC6871. In some embodiments the preferredhost of the present disclosure is C. glutamicum.

Suitable host strains of the genus Corynebacterium, in particular of thespecies Corynebacterium glutamicum, are in particular the knownwild-type strains: Corynebacterium glutamicum ATCC13032, Corynebacteriumacetoglutamicum ATCC 15806, Corynebacterium acetoacidophilum ATCC 13870,Corynebacterium melassecola ATCC17965, Corynebacterium thermoaminogenesFERM BP-1539, Brevibacterium flavum ATCC14067, Brevibacteriumlactofermentum ATCC13869, and Brevibacterium divaricatum ATCC14020; andL-amino acid-producing mutants, or strains, prepared therefrom, such as,for example, the L-lysine-producing strains: Corynebacterium glutamicumFERM-P 1709, Brevibacterium flavum FERM-P 1708, Brevibacteriumlactofermentum FERM-P 1712, Corynebacterium glutamicum FERM-P 6463,Corynebacterium glutamicum FERM-P 6464, Corynebacterium glutamicumDM58-1, Corynebacterium glutamicum DG52-5, Corynebacterium glutamicumDSM5714, and Corynebacterium glutamicum DSM12866.

The term “Micrococcus glutamicus” has also been in use for C.glutamicum. Some representatives of the species C. efficiens have alsobeen referred to as C. thermoaminogenes in the prior art, such as thestrain FERM BP-1539, for example.

In some embodiments, the host cell of the present disclosure is aeukaryotic cell. Suitable eukaryotic host cells include, but are notlimited to: fungal cells, algal cells, insect cells, animal cells, andplant cells. Suitable fungal host cells include, but are not limited to:Ascomycota, Basidiomycota, Deuteromycota, Zygomycota, Fungi imperfecti.Certain preferred fungal host cells include yeast cells and filamentousfungal cells. Suitable filamentous fungi host cells include, forexample, any filamentous forms of the subdivision Eumycotina andOomycota. (see, e.g., Hawksworth et al., In Ainsworth and Bisby'sDictionary of The Fungi, 8th edition, 1995, CAB International,University Press, Cambridge, UK, which is incorporated herein byreference). Filamentous fungi are characterized by a vegetative myceliumwith a cell wall composed of chitin, cellulose and other complexpolysaccharides. The filamentous fungi host cells are morphologicallydistinct from yeast.

In certain illustrative, but non-limiting embodiments, the filamentousfungal host cell may be a cell of a species of: Achlya, Acremonium,Aspergillus, Aureobasidium, Bjerkandera, Ceriporiopsis, Cephalosporium,Chrysosporium, Cochliobolus, Corynascus, Cryphonectria, Cryptococcus,Coprinus, Coriolus, Diplodia, Endothis, Fusarium, Gibberella,Gliocladium, Humicola, Hypocrea, Myceliophthora (e.g., Myceliophthorathermophila), Mucor, Neurospora, Penicillium, Podospora, Phlebia,Piromyces, Pyricularia, Rhizomucor, Rhizopus, Schizophyllum,Scytalidium, Sporotrichum, Talaromyces, Thermoascus, Thielavia,Tramates, Tolypocladium, Trichoderma, Verticillium, Volvariella, orteleomorphs, or anamorphs, and synonyms or taxonomic equivalentsthereof. In one embodiment, the filamentous fungus is selected from thegroup consisting of A. nidulans, A. oryzae, A. sojae, and Aspergilli ofthe A. niger Group. In an embodiment, the filamentous fungus isAspergillus niger.

In another embodiment, specific mutants of the fungal species are usedfor the methods and systems provided herein. In one embodiment, specificmutants of the fungal species are used which are suitable for thehigh-throughput and/or automated methods and systems provided herein.Examples of such mutants can be strains that protoplast very well;strains that produce mainly or, more preferably, only protoplasts with asingle nucleus; strains that regenerate efficiently in microtiterplates, strains that regenerate faster and/or strains that take uppolynucleotide (e.g., DNA) molecules efficiently, strains that producecultures of low viscosity such as, for example, cells that producehyphae in culture that are not so entangled as to prevent isolation ofsingle clones and/or raise the viscosity of the culture, strains thathave reduced random integration (e.g., disabled non-homologous endjoining pathway) or combinations thereof.

In yet another embodiment, a specific mutant strain for use in themethods and systems provided herein can be strains lacking a selectablemarker gene such as, for example, uridine-requiring mutant strains.These mutant strains can be either deficient in orotidine 5 phosphatedecarboxylase (OMPD) or orotate p-ribosyl transferase (OPRT) encoded bythe pyrG or pyrE gene, respectively (T. Goosen et al., Curr Genet. 1987,11:499 503; J. Begueret et al., Gene. 1984 32:487 92.

In one embodiment, specific mutant strains for use in the methods andsystems provided herein are strains that possess a compact cellularmorphology characterized by shorter hyphae and a more yeast-likeappearance.

Suitable yeast host cells include, but are not limited to: Candida,Hansenula, Saccharomyces, Schizosaccharomyces, Pichia, Kluyveromyces,and Yarrowia. In some embodiments, the yeast cell is Hansenulapolymorpha, Saccharomyces cerevisiae, Saccaromyces carlsbergensis,Saccharomyces diastaticus, Saccharomyces norbensis, Saccharomyceskluyveri, Schizosaccharomyces pombe, Pichia pastoris, Pichia finlandica,Pichia trehalophila, Pichia kodamae, Pichia membranaefaciens, Pichiaopuntiae, Pichia thermotolerans, Pichia salictaria, Pichia quercuum,Pichia pijperi, Pichia stipitis, Pichia methanolica, Pichia angusta,Kluyveromyces lactis, Candida albicans, or Yarrowia lipolytica.

In certain embodiments, the host cell is an algal cell such as,Chlamydomonas (e.g., C. Reinhardtii) and Phormidium (P. sp. ATCC29409).

In other embodiments, the host cell is a prokaryotic cell. Suitableprokaryotic cells include gram positive, gram negative, andgram-variable bacterial cells. The host cell may be a species of, butnot limited to: Agrobacterium, Alicyclobacillus, Anabaena, Anacystis,Acinetobacter, Acidothermus, Arthrobacter, Azobacter, Bacillus,Bifidobacterium, Brevibacterium, Butyrivibrio, Buchnera, Campestris,Camplyobacter, Clostridium, Corynebacterium, Chromatium, Coprococcus,Escherichia, Enterococcus, Enterobacter, Erwinia, Fusobacterium,Faecalibacterium, Francisella, Flavobacterium, Geobacillus, Haemophilus,Helicobacter, Klebsiella, Lactobacillus, Lactococcus, Ilyobacter,Micrococcus, Microbacterium, Mesorhizobium, Methylobacterium,Methylobacterium, Mycobacterium, Neisseria, Pantoea, Pseudomonas,Prochlorococcus, Rhodobacter, Rhodopseudomonas, Rhodopseudomonas,Roseburia, Rhodospirillum, Rhodococcus, Scenedesmus, Streptomyces,Streptococcus, Synecoccus, Saccharomonospora, Saccharopolyspora,Staphylococcus, Serratia, Salmonella, Shigella, Thermoanaerobacterium,Tropheryma, Tularensis, Temecula, Thermosynechococcus, Thermococcus,Ureaplasma, Xanthomonas, Xylella, Yersinia, and Zymomonas. In someembodiments, the host cell is Corynebacterium glutamicum.

In some embodiments, the bacterial host strain is an industrial strain.Numerous bacterial industrial strains are known and suitable in themethods and compositions described herein.

In some embodiments, the bacterial host cell is of the Agrobacteriumspecies (e.g., A. radiobacter, A. rhizogenes, A. rubi), the Arthrobacterspecies (e.g., A. aurescens, A. citreus, A. globformis, A.hydrocarboglutamicus, A. mysorens, A. nicotianae, A. paraffineus, A.protophonniae, A. roseoparaffinus, A. sulfureus, A. ureafaciens), theBacillus species (e.g., B. thuringiensis, B. anthracis, B. megaterium,B. subtilis, B. lentus, B. circulars, B. pumilus, B. lautus, B.coagulans, B. brevis, B. firmus, B. alkaophius, B. licheniformis, B.clausii, B. stearothermophilus, B. halodurans and B. amyloliquefaciens.In particular embodiments, the host cell will be an industrial Bacillusstrain including but not limited to B. subtilis, B. pumilus, B.licheniformis, B. megaterium, B. clausii, B. stearothermophilus and B.amyloliquefaciens. In some embodiments, the host cell will be anindustrial Clostridium species (e.g., C. acetobutylicum, C. tetani E88,C. lituseburense, C. saccharobutylicum, C. perfringens, C.beijerinckii). In some embodiments, the host cell will be an industrialCorynebacterium species (e.g., C. glutamicum, C. acetoacidophilum). Insome embodiments, the host cell will be an industrial Escherichiaspecies (e.g., E. coli). In some embodiments, the host cell will be anindustrial Erwinia species (e.g., E. uredovora, E. carotovora, E.ananas, E. herbicola, E. punctata, E. terreus). In some embodiments, thehost cell will be an industrial Pantoea species (e.g., P. citrea, P.agglomerans). In some embodiments, the host cell will be an industrialPseudomonas species, (e.g., P. putida, P. aeruginosa, P. mevalonii). Insome embodiments, the host cell will be an industrial Streptococcusspecies (e.g., S. equisimiles, S. pyogenes, S. uberis). In someembodiments, the host cell will be an industrial Streptomyces species(e.g., S. ambofaciens, S. achromogenes, S. avermitilis, S. coelicolor,S. aureofaciens, S. aureus, S. fungicidicus, S. griseus, S. lividans).In some embodiments, the host cell will be an industrial Zymomonasspecies (e.g., Z. mobilis, Z. lipolytica), and the like.

The present disclosure is also suitable for use with a variety of animalcell types, including mammalian cells, for example, human (including293, WI38, PER.C6 and Bowes melanoma cells), mouse (including 3T₃, NS0,NS1, Sp2/0), hamster (CHO, BHK), monkey (COS, FRhL, Vero), and hybridomacell lines.

In various embodiments, strains that may be used in the practice of thedisclosure including both prokaryotic and eukaryotic strains, arereadily accessible to the public from a number of culture collectionssuch as American Type Culture Collection (ATCC), Deutsche Sammlung vonMikroorganismen and Zellkulturen GmbH (DSM), Centraalbureau VoorSchimmelcultures (CBS), and Agricultural Research Service Patent CultureCollection, Northern Regional Research Center (NRRL).

In some embodiments, the methods of the present disclosure are alsoapplicable to multi-cellular organisms. For example, the platform couldbe used for improving the performance of crops. The organisms cancomprise a plurality of plants such as Gramineae, Fetucoideae,Poacoideae, Agrostis, Phleum, Dactylis, Sorgum, Setaria, Zea, Oryza,Triticum, Secale, Avena, Hordeum, Saccharum, Poa, Festuca, Stenotaphrum,Cynodon, Coix, Olyreae, Phareae, Compositae or Leguminosae. For example,the plants can be corn, rice, soybean, cotton, wheat, rye, oats, barley,pea, beans, lentil, peanut, yam bean, cowpeas, velvet beans, clover,alfalfa, lupine, vetch, lotus, sweet clover, wisteria, sweet pea,sorghum, millet, sunflower, canola or the like. Similarly, the organismscan include a plurality of animals such as non-human mammals, fish,insects, or the like.

Generating Genetic Diversity Pools for Utilization in the Genetic Design& HTP Microbial Engineering Platform

In some embodiments, the methods of the present disclosure arecharacterized as genetic design. As used herein, the term genetic designrefers to the reconstruction or alteration of a host organism's genomethrough the identification and selection of the most optimum variants ofa particular gene, portion of a gene, promoter, stop codon, 5′UTR,3′UTR, or other DNA sequence to design and create new superior hostcells.

In some embodiments, a first step in the genetic design methods of thepresent disclosure is to obtain an initial genetic diversity poolpopulation with a plurality of sequence variations from which a new hostgenome may be reconstructed.

In some embodiments, a subsequent step in the genetic design methodstaught herein is to use one or more of the aforementioned HTP moleculartool sets (e.g. SNP swapping or promoter swapping) to construct HTPgenetic design libraries, which then function as drivers of the genomicengineering process, by providing libraries of particular genomicalterations for testing in a host cell.

Harnessing Diversity Pools from Existing Wild-Type Strains

In some embodiments, the present disclosure teaches methods foridentifying the sequence diversity present among microbes of a givenwild-type population. Therefore, a diversity pool can be a given numbern of wild-type microbes utilized for analysis, with said microbes'genomes representing the “diversity pool.”

In some embodiments, the diversity pools can be the result of existingdiversity present in the natural genetic variation among said wild-typemicrobes. This variation may result from strain variants of a given hostcell or may be the result of the microbes being different speciesentirely. Genetic variations can include any differences in the geneticsequence of the strains, whether naturally occurring or not. In someembodiments, genetic variations can include SNPs swaps, PRO swaps,Start/Stop Codon swaps, or STOP swaps, among others.

Harnessing Diversity Pools from Existing Industrial Strain Variants

In other embodiments of the present disclosure, diversity pools arestrain variants created during traditional strain improvement processes(e.g., one or more host organism strains generated via random mutationand selected for improved yields over the years). Thus, in someembodiments, the diversity pool or host organisms can comprise acollection of historical production strains.

In particular aspects, a diversity pool may be an original parentmicrobial strain (S₁) with a “baseline” genetic sequence at a particulartime point (S₁Gen₁) and then any number of subsequent offspring strains(S₂, S₃, S₄, S₅, etc., generalizable to S_(2-n)) that werederived/developed from said S₁ strain and that have a different genome(S_(2-n)Gen_(2-n)), in relation to the baseline genome of S₁.

For example, in some embodiments, the present disclosure teachessequencing the microbial genomes in a diversity pool to identify theSNP's present in each strain. In one embodiment, the strains of thediversity pool are historical microbial production strains. Thus, adiversity pool of the present disclosure can include for example, anindustrial base strain, and one or more mutated industrial strainsproduced via traditional strain improvement programs.

Once all SNPs in the diversity pool are identified, the presentdisclosure teaches methods of SNP swapping and screening methods todelineate (i.e. quantify and characterize) the effects (e.g. creation ofa phenotype of interest) of SNPs individually and in groups. Thus, asaforementioned, an initial step in the taught platform can be to obtainan initial genetic diversity pool population with a plurality ofsequence variations, e.g. SNPs. Then, a subsequent step in the taughtplatform can be to use one or more of the aforementioned HTP moleculartool sets (e.g. SNP swapping) to construct HTP genetic design libraries,which then function as drivers of the genomic engineering process, byproviding libraries of particular genomic alterations for testing in amicrobe.

In some embodiments, the SNP swapping methods of the present disclosurecomprise the step of introducing one or more SNPs identified in amutated strain (e.g., a strain from amongst S_(2-n)Gen_(2-n)) to a basestrain (S₁Gen₁) or wild-type strain.

In other embodiments, the SNP swapping methods of the present disclosurecomprise the step of removing one or more SNPs identified in a mutatedstrain (e.g., a strain from amongst S_(2-n)Gen_(2-n)).

Creating Diversity Pools Via Mutagenesis

In some embodiments, the mutations of interest in a given diversity poolpopulation of cells can be artificially generated by any means formutating strains, including mutagenic chemicals, or radiation. The term“mutagenizing” is used herein to refer to a method for inducing one ormore genetic modifications in cellular nucleic acid material.

The term “genetic modification” refers to any alteration of DNA.Representative gene modifications include nucleotide insertions,deletions, substitutions, and combinations thereof, and can be as smallas a single base or as large as tens of thousands of bases. Thus, theterm “genetic modification” encompasses inversions of a nucleotidesequence and other chromosomal rearrangements, whereby the position ororientation of DNA comprising a region of a chromosome is altered. Achromosomal rearrangement can comprise an intrachromosomal rearrangementor an interchromosomal rearrangement.

In one embodiment, the mutagenizing methods employed in the presentlyclaimed subject matter are substantially random such that a geneticmodification can occur at any available nucleotide position within thenucleic acid material to be mutagenized. Stated another way, in oneembodiment, the mutagenizing does not show a preference or increasedfrequency of occurrence at particular nucleotide sequences.

The methods of the disclosure can employ any mutagenic agent including,but not limited to: ultraviolet light, X-ray radiation, gamma radiation,N-ethyl-N-nitrosourea (ENU), methyinitrosourea (MNU), procarbazine(PRC), triethylene melamine (TEM), acrylamide monomer (AA), chlorambucil(CHL), melphalan (MLP), cyclophosphamide (CPP), diethyl sulfate (DES),ethyl methane sulfonate (EMS), methyl methane sulfonate (MMS),6-mercaptopurine (6-MP), mitomycin-C(MMC),N-methyl-N′-nitro-N-nitrosoguanidine (MNNG), ³H₂O, and urethane (UR)(See e.g., Rinchik, 1991; Marker et al., 1997; and Russell, 1990).Additional mutagenic agents are well known to persons having skill inthe art, including those described inhttp://www.iephb.nw.ru/˜spirov/hazard/mutagen_lst.html.

The term “mutagenizing” also encompasses a method for altering (e.g., bytargeted mutation) or modulating a cell function, to thereby enhance arate, quality, or extent of mutagenesis. For example, a cell can bealtered or modulated to thereby be dysfunctional or deficient in DNArepair, mutagen metabolism, mutagen sensitivity, genomic stability, orcombinations thereof. Thus, disruption of gene functions that normallymaintain genomic stability can be used to enhance mutagenesis.Representative targets of disruption include, but are not limited to DNAligase I (Bentley et al., 2002) and casein kinase I (U.S. Pat. No.6,060,296).

In some embodiments, site-specific mutagenesis (e.g., primer-directedmutagenesis using a commercially available kit such as the TransformerSite Directed mutagenesis kit (Clontech)) is used to make a plurality ofchanges throughout a nucleic acid sequence in order to generate nucleicacid encoding a cleavage enzyme of the present disclosure.

The frequency of genetic modification upon exposure to one or moremutagenic agents can be modulated by varying dose and/or repetition oftreatment, and can be tailored for a particular application.

Thus, in some embodiments, “mutagenesis” as used herein comprises alltechniques known in the art for inducing mutations, includingerror-prone PCR mutagenesis, oligonucleotide-directed mutagenesis,site-directed mutagenesis, and iterative sequence recombination by anyof the techniques described herein.

Single Locus Mutations to Generate Diversity

In some embodiments, the present disclosure teaches mutating cellpopulations by introducing, deleting, or replacing selected portions ofgenomic DNA. Thus, in some embodiments, the present disclosure teachesmethods for targeting mutations to a specific locus. In otherembodiments, the present disclosure teaches the use of gene editingtechnologies such as ZFNs, TALENS, or CRISPR, to selectively edit targetDNA regions.

In other embodiments, the present disclosure teaches mutating selectedDNA regions outside of the host organism, and then inserting the mutatedsequence back into the host organism. For example, in some embodiments,the present disclosure teaches mutating native or synthetic promoters toproduce a range of promoter variants with various expression properties(see promoter ladder infra). In other embodiments, the presentdisclosure is compatible with single gene optimization techniques, suchas ProSAR (Fox et al. 2007. “Improving catalytic function byProSAR-driven enzyme evolution.” Nature Biotechnology Vol 25 (3)338-343, incorporated by reference herein).

In some embodiments, the selected regions of DNA are produced in vitrovia gene shuffling of natural variants, or shuffling with syntheticoligos, plasmid-plasmid recombination, virus plasmid recombination,virus-virus recombination. In other embodiments, the genomic regions areproduced via error-prone PCR (see e.g., FIG. 1).

In some embodiments, generating mutations in selected genetic regions isaccomplished by “reassembly PCR.” Briefly, oligonucleotide primers(oligos) are synthesized for PCR amplification of segments of a nucleicacid sequence of interest, such that the sequences of theoligonucleotides overlap the junctions of two segments. The overlapregion is typically about 10 to 100 nucleotides in length. Each of thesegments is amplified with a set of such primers. The PCR products arethen “reassembled” according to assembly protocols. In brief, in anassembly protocol, the PCR products are first purified away from theprimers, by, for example, gel electrophoresis or size exclusionchromatography. Purified products are mixed together and subjected toabout 1-10 cycles of denaturing, reannealing, and extension in thepresence of polymerase and deoxynucleoside triphosphates (dNTP's) andappropriate buffer salts in the absence of additional primers(“self-priming”). Subsequent PCR with primers flanking the gene are usedto amplify the yield of the fully reassembled and shuffled genes.

In some embodiments of the disclosure, mutated DNA regions, such asthose discussed above, are enriched for mutant sequences so that themultiple mutant spectrum, i.e. possible combinations of mutations, ismore efficiently sampled. In some embodiments, mutated sequences areidentified via a mutS protein affinity matrix (Wagner et al., NucleicAcids Res. 23(19):3944-3948 (1995); Su et al., Proc. Natl. Acad. Sci.(U.S.A.), 83:5057-5061(1986)) with a preferred step of amplifying theaffinity-purified material in vitro prior to an assembly reaction. Thisamplified material is then put into an assembly or reassembly PCRreaction as described in later portions of this application.

Promoter Ladders

Promoters regulate the rate at which genes are transcribed and caninfluence transcription in a variety of ways. Constitutive promoters,for example, direct the transcription of their associated genes at aconstant rate regardless of the internal or external cellularconditions, while regulatable promoters increase or decrease the rate atwhich a gene is transcribed depending on the internal and/or theexternal cellular conditions, e.g. growth rate, temperature, responsesto specific environmental chemicals, and the like. Promoters can beisolated from their normal cellular contexts and engineered to regulatethe expression of virtually any gene, enabling the effectivemodification of cellular growth, product yield and/or other phenotypesof interest.

In some embodiments, the present disclosure teaches methods forproducing promoter ladder libraries for use in downstream genetic designmethods. For example, in some embodiments, the present disclosureteaches methods of identifying one or more promoters and/or generatingvariants of one or more promoters within a host cell, which exhibit arange of expression strengths, or superior regulatory properties. Aparticular combination of these identified and/or generated promoterscan be grouped together as a promoter ladder, which is explained in moredetail below.

In some embodiments, the present disclosure teaches the use of promoterladders. In some embodiments, the promoter ladders of the presentdisclosure comprise promoters exhibiting a continuous range ofexpression profiles. For example, in some embodiments, promoter laddersare created by: identifying natural, native, or wild-type promoters thatexhibit a range of expression strengths in response to a stimuli, orthrough constitutive expression (see e.g., FIG. 20 and FIGS. 28-30).These identified promoters can be grouped together as a promoter ladder.

In other embodiments, the present disclosure teaches the creation ofpromoter ladders exhibiting a range of expression profiles acrossdifferent conditions. For example, in some embodiments, the presentdisclosure teaches creating a ladder of promoters with expression peaksspread throughout the different stages of a fermentation (see e.g., FIG.28). In other embodiments, the present disclosure teaches creating aladder of promoters with different expression peak dynamics in responseto a specific stimulus (see e.g., FIG. 29). Persons skilled in the artwill recognize that the regulatory promoter ladders of the presentdisclosure can be representative of any one or more regulatory profiles.

In some embodiments, the promoter ladders of the present disclosure aredesigned to perturb gene expression in a predictable manner across acontinuous range of responses. In some embodiments, the continuousnature of a promoter ladder confers strain improvement programs withadditional predictive power. For example, in some embodiments, swappingpromoters or termination sequences of a selected metabolic pathway canproduce a host cell performance curve, which identifies the most optimumexpression ratio or profile; producing a strain in which the targetedgene is no longer a limiting factor for a particular reaction or geneticcascade, while also avoiding unnecessary over expression ormisexpression under inappropriate circumstances. In some embodiments,promoter ladders are created by: identifying natural, native, orwild-type promoters exhibiting the desired profiles. In otherembodiments, the promoter ladders are created by mutating naturallyoccurring promoters to derive multiple mutated promoter sequences. Eachof these mutated promoters is tested for effect on target geneexpression. In some embodiments, the edited promoters are tested forexpression activity across a variety of conditions, such that eachpromoter variant's activity is documented/characterized/annotated andstored in a database. The resulting edited promoter variants aresubsequently organized into promoter ladders arranged based on thestrength of their expression (e.g., with highly expressing variants nearthe top, and attenuated expression near the bottom, therefore leading tothe term “ladder”).

In some embodiments, the present disclosure teaches promoter laddersthat are a combination of identified naturally occurring promoters andmutated variant promoters.

In some embodiments, the present disclosure teaches methods ofidentifying natural, native, or wild-type promoters that satisfied bothof the following criteria: 1) represented a ladder of constitutivepromoters; and 2) could be encoded by short DNA sequences, ideally lessthan 100 base pairs. In some embodiments, constitutive promoters of thepresent disclosure exhibit constant gene expression across two selectedgrowth conditions (typically compared among conditions experiencedduring industrial cultivation). In some embodiments, the promoters ofthe present disclosure will consist of a ˜60 base pair core promoter,and a 5′ UTR between 26- and 40 base pairs in length.

In some embodiments, one or more of the aforementioned identifiednaturally occurring promoter sequences are chosen for gene editing. Insome embodiments, the natural promoters are edited via any of themutation methods described supra. In other embodiments, the promoters ofthe present disclosure are edited by synthesizing new promoter variantswith the desired sequence.

The entire disclosure of U.S. Patent Application No. 62/264,232, filedon Dec. 7, 2015, is hereby incorporated by reference in its entirety forall purposes

A non-exhaustive list of the promoters of the present disclosure isprovided in the below Table 1. Each of the promoter sequences can bereferred to as a heterologous promoter or heterologous promoterpolynucleotide.

TABLE 1 Selected promoter sequences of the present disclosure. SEQPromoter Promoter ID No. Short Name Name 1 P1 Pcg0007_lib_39 2 P2Pcg0007 3 P3 Pcg1860 4 P4 Pcg0755 5 P5 Pcg0007_265 6 P6 Pcg3381 7 P7Pcg0007_119 8 P8 Pcg3121

In some embodiments, the promoters of the present disclosure exhibit atleast 100%, 99%, 98%, 97%, 96%, 95%, 94%, 93%, 92%, 91%, 90%, 89%, 88%,87%, 86%, 85%, 84%, 83%, 82%, 81%, 80%, 79%, 78%, 77%, 76%, or 75%sequence identity with a promoter from the above table.

Terminator Ladders

In some embodiments, the present disclosure teaches methods of improvinggenetically engineered host strains by providing one or moretranscriptional termination sequences at a position 3′ to the end of theRNA encoding element. In some embodiments, the present disclosureteaches that the addition of termination sequences improves theefficiency of RNA transcription of a selected gene in the geneticallyengineered host. In other embodiments, the present disclosure teachesthat the addition of termination sequences reduces the efficiency of RNAtranscription of a selected gene in the genetically engineered host.Thus in some embodiments, the terminator ladders of the presentdisclosure comprises a series of terminator sequences exhibiting a rangeof transcription efficiencies (e.g., one weak terminator, one averageterminator, and one strong promoter).

A transcriptional termination sequence may be any nucleotide sequence,which when placed transcriptionally downstream of a nucleotide sequenceencoding an open reading frame, causes the end of transcription of theopen reading frame. Such sequences are known in the art and may be ofprokaryotic, eukaryotic or phage origin. Examples of terminatorsequences include, but are not limited to, PTH-terminator, pET-T7terminator, T3-Tφ terminator, pBR322-P4 terminator, vesicular stomatitusvirus terminator, rrnB-T1 terminator, rrnC terminator, TTadctranscriptional terminator, and yeast-recognized termination sequences,such as Mata (α-factor) transcription terminator, native α-factortranscription termination sequence, ADR1 transcription terminationsequence, ADH2 transcription termination sequence, and GAPDtranscription termination sequence. A non-exhaustive listing oftranscriptional terminator sequences may be found in the iGEM registry,which is available at: http://partsregistry.org/Terminators/Catalog.

In some embodiments, transcriptional termination sequences may bepolymerase-specific or nonspecific, however, transcriptional terminatorsselected for use in the present embodiments should form a ‘functionalcombination’ with the selected promoter, meaning that the terminatorsequence should be capable of terminating transcription by the type ofRNA polymerase initiating at the promoter. For example, in someembodiments, the present disclosure teaches a eukaryotic RNA pol IIpromoter and eukaryotic RNA pol II terminators, a T7 promoter and T7terminators, a T3 promoter and T3 terminators, a yeast-recognizedpromoter and yeast-recognized termination sequences, etc., wouldgenerally form a functional combination. The identity of thetranscriptional termination sequences used may also be selected based onthe efficiency with which transcription is terminated from a givenpromoter. For example, a heterologous transcriptional terminatorsequence may be provided transcriptionally downstream of the RNAencoding element to achieve a termination efficiency of at least 60%, atleast 70%, at least 75%, at least 80%, at least 85%, at least 90%, atleast 91%, at least 92%, at least 93%, at least 94%, at least 95%, atleast 96%, at least 9′7%, at least 98%, or at least 99% from a givenpromoter.

In some embodiments, efficiency of RNA transcription from the engineeredexpression construct can be improved by providing nucleic acid sequenceforms a secondary structure comprising two or more hairpins at aposition 3′ to the end of the RNA encoding element. Not wishing to bebound by a particular theory, the secondary structure destabilizes thetranscription elongation complex and leads to the polymerase becomingdissociated from the DNA template, thereby minimizing unproductivetranscription of non-functional sequence and increasing transcription ofthe desired RNA. Accordingly, a termination sequence may be providedthat forms a secondary structure comprising two or more adjacenthairpins. Generally, a hairpin can be formed by a palindromic nucleotidesequence that can fold back on itself to form a paired stem region whosearms are connected by a single stranded loop. In some embodiments, thetermination sequence comprises 2, 3, 4, 5, 6, 7, 8, 9, 10 or moreadjacent hairpins. In some embodiments, the adjacent hairpins areseparated by 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15unpaired nucleotides. In some embodiments, a hairpin stem comprises 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,24, 25, 26, 27, 28, 29, 30 or more base pairs in length. In certainembodiments, a hairpin stem is 12 to 30 base pairs in length. In certainembodiments, the termination sequence comprises two or more medium-sizedhairpins having stem region comprising about 9 to 25 base pairs. In someembodiments, the hairpin comprises a loop-forming region of 1, 2, 3, 4,5, 6, 7, 8, 9, or 10 nucleotides. In some embodiments, the loop-formingregion comprises 4-8 nucleotides. Not wishing to be bound by aparticular theory, stability of the secondary structure can becorrelated with termination efficiency. Hairpin stability is determinedby its length, the number of mismatches or bulges it contains and thebase composition of the paired region. Pairings between guanine andcytosine have three hydrogen bonds and are more stable compared toadenine-thymine pairings, which have only two. The G/C content of ahairpin-forming palindromic nucleotide sequence can be at least 60%, atleast 65%, at least 70%, at least 75%, at least 80%, at least 85%, atleast 90% or more. In some embodiments, the G/C content of ahairpin-forming palindromic nucleotide sequence is at least 80%. In someembodiments, the termination sequence is derived from one or moretranscriptional terminator sequences of prokaryotic, eukaryotic or phageorigin. In some embodiments, a nucleotide sequence encoding a series of4, 5, 6, 7, 8, 9, 10 or more adenines (A) are provided 3′ to thetermination sequence.

In some embodiments, the present disclosure teaches the use of a seriesof tandem termination sequences. In some embodiments, the firsttranscriptional terminator sequence of a series of 2, 3, 4, 5, 6, 7, ormore may be placed directly 3′ to the final nucleotide of the dsRNAencoding element or at a distance of at least 1-5, 5-10, 10-15, 15-20,20-25, 25-30, 30-35, 35-40, 40-45, 45-50, 50-100, 100-150, 150-200,200-300, 300-400, 400-500, 500-1,000 or more nucleotides 3′ to the finalnucleotide of the dsRNA encoding element. The number of nucleotidesbetween tandem transcriptional terminator sequences may be varied, forexample, transcriptional terminator sequences may be separated by 0, 1,2, 3, 4, 5, 6, 7, 8, 9, 10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40,40-45, 45-50 or more nucleotides. In some embodiments, thetranscriptional terminator sequences may be selected based on theirpredicted secondary structure as determined by a structure predictionalgorithm. Structural prediction programs are well known in the art andinclude, for example, CLC Main Workbench.

Persons having skill in the art will recognize that the methods of thepresent disclosure are compatible with any termination sequence. In someembodiments, the present disclosure teaches use of annotatedCorynebacterium glutamicum terminators as disclosed in fromPfeifer-Sancar et al. 2013. “Comprehensive analysis of theCorynebacterium glutamicum transcriptome using an improved RNAseqtechnique” Pfeifer-Sancar et al. BMC Genomics 2013, 14:888). In otherembodiments, the present disclosure teaches use of transcriptionalterminator sequences found in the iGEM registry, which is available at:http://partsregistry.org/Terminators/Catalog. A non-exhaustive listingof transcriptional terminator sequences of the present disclosure isprovided in Table 1.1 below.

TABLE 1.1 Non-exhaustive list of termination sequences of the presentdisclosure. E. coli Name Description Direction Length BBa_B0010 T1 fromE. coli rrnB Forward 80 BBa_B0012 TE from coliphageT7 Forward 41BBa_B0013 TE from coliphage T7 (+/−) Forward 47 BBa_B0015 doubleterminator (B0010-B0012) Forward 129 BBa_B0017 double terminator(B0010-B0010) Forward 168 BBa_B0053 Terminator (His) Forward 72BBa_B0055 -- No description -- 78 BBa_B1002 Terminator (artificial,Forward 34 small, % T~= 85%) BBa_B1003 Terminator (artificial, small, %T~= 80) Forward 34 BBa_B1004 Terminator (artificial, small, % T~= 55)Forward 34 BBa_B1005 Terminator (artificial, Forward 34 small, % T~= 25%BBa_ B1006 Terminator (artificial, large, % T~>90) Forward 39 BBa_B1010Terninator (artificial, large, % T~<10) Forward 40 BBa_I11013Modification of biobricks part 129 BBa_B0015 BBa_I51003 --No description-- 110 BBa_J61048 [mpB-T1] Terminator Forward 113 BBa_K1392970Terminator + Tetr Promoter + T4 623 Endolysin BBa_K1486001 Arabinosepromoter + CpxR Forward 1924 BBa_K1486005 Arabinose promoter +sfGFP-CpxR Forward 2668 [Cterm] BBa_K1486009 CxpR & Split IFP1.4[Nterm + Nterm] Forward 3726 BBa_K780000 Terminator for Bacillussubtilis 54 BBa_K864501 T22, P22 late terminator Forward 42 BBa_K864600T0 (21 imm) transcriptional terminator Forward 52 BBa_K864601 Lambda t1transcriptional terminator Forward BBa_B0011 LuxICDABEG (+/−)Bidirectional 46 BBa_B0014 double terminator (B0012-B0011) Bidirectional95 BBa_B0021 LuxICDABEG (+/−), reversed Bidirectional 46 BBa_B0024double terminator (B0012-B0011), Bidirectional 95 reversed BBa_B0050Terminator (pBR322, +/−) Bidirectional 33 BBa_B0051 Terminator(yciA/tonA, +/−) Bidirectional 35 BBa_B1001 Terminator (artifical,small, % T~= 90) Bidirectional 34 BBa_B1007 Terminator (artificial,large, % T~= 80) Bidirectional 40 BBa_B1008 Terminator (artificial,large, % T~= 70) Bidirectional 40 BBa_B1009 Terminator (artificial,Bidirectional 40 large, % T~= 40%) BBa_K187025 terminator in pAB,BioBytes plasmid 60 BBa_K259006 GFP-Terminator Bidirectional 823BBa_B0020 Terminator (Reverse B0010) Reverse 82 BBa_B0022 TE fromcoliphageT7, reversed Reverse 41 BBa_B0023 TE from coliphage T7,reversed Reverse 47 BBa_B0025 double terminator (B0015), reversedReverse 129 BBa_B0052 Terminator (rrnC) Forward 41 BBa_B0060 Terminator(Reverse B0050) Bidirectional 33 BBa_B0061 Terminator (Reverse B0051)Bidirectional 35 BBa_B0063 Terminator (Reverse B0053) Reverse 72 Yeastand other Eukaryotes BBa_J63002 ADH1 terminator from S. cerevisiaeForward 225 BBa_K110012 STE2 terminator Forward 123 BBa_K1462070 cyc1250 BBa_K1486025 ADH1 Terminator Forward 188 BBa_K392003 yeast ADH1terminator 129 BBa_K801011 TEF1 yeast terminator 507 BBa_K801012 ADH1yeast terminator 349 BBa_Y1015 CycE1 252 BBa_J52016 eukaryotic--derivedfrom SV40 early Forward 238 poly A signal sequence BBa_J63002 ADH1terminator from S. cerevisiae Forward 225 BBa_K110012 STE2 terminatorForward 123 BBa_K1159307 35S Terminator of Cauliflower Mosaic 217 Virus(CaMV) BBa_K1462070 cyc1 250 BBa_K1484215 nopaline synthase terminator293 BBa_K1486025 ADH1 Terminator Forward 188 BBa_K392003 yeast ADH1terminator 129 BBa_K404108 hGH terminator 481 BBa_K404116hGH_[AAV2]-right-ITR 632 BBa_K678012 SV40 poly A, terminator for 139mammalian cells BBa_K678018 hGH poly A, terminator for 635 mammaliancells BBa_K678019 BGH poly A, mammalian terminator 233 BBa_K678036 trpCterminator for Aspergillus 759 nidulans BBa_K678037 T1-motni, terminatorfor Aspergillus 1006 niger BBa_K678038 T2-motni, terminator forAspergillus 990 niger BBa_K678039 T3-motni, terminator for Aspergillus889 niger BBa_K801011 TEF1 yeast terminator 507 BBa_K801012 ADH1 yeastterminator 349 BBa_Y1015 CycE1 252 Corynebacterium Terminator TerminatorTranscript DNA Terminator Start End strand End Sequence cg0001 16281647 + loop SEQ ID NO: 9 T1 cg0007 7504 7529 + stem 1 SEQ ID NO: 10 T2cg0371 322229 322252 + stem 1 SEQ ID NO: 11 T3 cg0480 421697 421720 −stem 1 SEQ ID NO: 12 T4 cg0494 436587 436608 + loop SEQ ID NO: 13 T5cg0564 499895 499917 + stem 1 SEQ ID NO: 14 T6 cg0610 541016 541039 +stem 2 SEQ ID NO: 15 T7 cg0695 613847 613868 − loop SEQ ID NO: 16 T8

Hypothesis-Driven Diversity Pools and Hill Climbing

The present disclosure teaches that the HTP genomic engineering methodsof the present disclosure do not require prior genetic knowledge inorder to achieve significant gains in host cell performance. Indeed, thepresent disclosure teaches methods of generating diversity pools viaseveral functionally agnostic approaches, including random mutagenesis,and identification of genetic diversity among pre-existing host cellvariants (e.g., such as the comparison between a wild type host cell andan industrial variant).

In some embodiments however, the present disclosure also teacheshypothesis-driven methods of designing genetic diversity mutations thatwill be used for downstream HTP engineering. That is, in someembodiments, the present disclosure teaches the directed design ofselected mutations. In some embodiments, the directed mutations areincorporated into the engineering libraries of the present disclosure(e.g., SNP swap, PRO swap, or STOP swap).

In some embodiments, the present disclosure teaches the creation ofdirected mutations based on gene annotation, hypothesized (or confirmed)gene function, or location within a genome. The diversity pools of thepresent disclosure may include mutations in genes hypothesized to beinvolved in a specific metabolic or genetic pathway associated in theliterature with increased performance of a host cell. In otherembodiments, the diversity pool of the present disclosure may alsoinclude mutations to genes present in an operon associated with improvedhost performance. In yet other embodiments, the diversity pool of thepresent disclosure may also include mutations to genes based onalgorithmic predicted function, or other gene annotation.

In some embodiments, the present disclosure teaches a “shell” basedapproach for prioritizing the targets of hypothesis-driven mutations.The shell metaphor for target prioritization is based on the hypothesisthat only a handful of primary genes are responsible for most of aparticular aspect of a host cell's performance (e.g., production of asingle biomolecule). These primary genes are located at the core of theshell, followed by secondary effect genes in the second layer, tertiaryeffects in the third shell, and . . . etc. For example, in oneembodiment the core of the shell might comprise genes encoding criticalbiosynthetic enzymes within a selected metabolic pathway (e.g.,production of citric acid). Genes located on the second shell mightcomprise genes encoding for other enzymes within the biosyntheticpathway responsible for product diversion or feedback signaling. Thirdtier genes under this illustrative metaphor would likely compriseregulatory genes responsible for modulating expression of thebiosynthetic pathway, or for regulating general carbon flux within thehost cell.

The present disclosure also teaches “hill climb” methods for optimizingperformance gains from every identified mutation. In some embodiments,the present disclosure teaches that random, natural, orhypothesis-driven mutations in HTP diversity libraries can result in theidentification of genes associated with host cell performance. Forexample, the present methods may identify one or more beneficial SNPslocated on, or near, a gene coding sequence. This gene might beassociated with host cell performance, and its identification can beanalogized to the discovery of a performance “hill” in the combinatorialgenetic mutation space of an organism.

In some embodiments, the present disclosure teaches methods of exploringthe combinatorial space around the identified hill embodied in the SNPmutation. That is, in some embodiments, the present disclosure teachesthe perturbation of the identified gene and associated regulatorysequences in order to optimize performance gains obtained from that genenode (i.e., hill climbing). Thus, according to the methods of thepresent disclosure, a gene might first be identified in a diversitylibrary sourced from random mutagenesis, but might be later improved foruse in the strain improvement program through the directed mutation ofanother sequence within the same gene.

The concept of hill climbing can also be expanded beyond the explorationof the combinatorial space surrounding a single gene sequence. In someembodiments, a mutation in a specific gene might reveal the importanceof a particular metabolic or genetic pathway to host cell performance.For example, in some embodiments, the discovery that a mutation in asingle RNA degradation gene resulted in significant host performancegains could be used as a basis for mutating related RNA degradationgenes as a means for extracting additional performance gains from thehost organism. Persons having skill in the art will recognize variantsof the above describe shell and hill climb approaches to directedgenetic design. High-throughput Screening.

Cell Culture and Fermentation

Cells of the present disclosure can be cultured in conventional nutrientmedia modified as appropriate for any desired biosynthetic reactions orselections. In some embodiments, the present disclosure teaches culturein inducing media for activating promoters. In some embodiments, thepresent disclosure teaches media with selection agents, includingselection agents of transformants (e.g., antibiotics), or selection oforganisms suited to grow under inhibiting conditions (e.g., high ethanolconditions). In some embodiments, the present disclosure teaches growingcell cultures in media optimized for cell growth. In other embodiments,the present disclosure teaches growing cell cultures in media optimizedfor product yield. In some embodiments, the present disclosure teachesgrowing cultures in media capable of inducing cell growth and alsocontains the necessary precursors for final product production (e.g.,high levels of sugars for ethanol production).

Culture conditions, such as temperature, pH and the like, are thosesuitable for use with the host cell selected for expression, and will beapparent to those skilled in the art. As noted, many references areavailable for the culture and production of many cells, including cellsof bacterial, plant, animal (including mammalian) and archaebacterialorigin. See e.g., Sambrook, Ausubel (all supra), as well as Berger,Guide to Molecular Cloning Techniques, Methods in Enzymology volume 152Academic Press, Inc., San Diego, Calif.; and Freshney (1994) Culture ofAnimal Cells, a Manual of Basic Technique, third edition, Wiley-Liss,New York and the references cited therein; Doyle and Griffiths (1997)Mammalian Cell Culture: Essential Techniques John Wiley and Sons, NY;Humason (1979) Animal Tissue Techniques, fourth edition W.H. Freeman andCompany; and Ricciardelle et al., (1989) In Vitro Cell Dev. Biol.25:1016-1024, all of which are incorporated herein by reference. Forplant cell culture and regeneration, Payne et al. (1992) Plant Cell andTissue Culture in Liquid Systems John Wiley & Sons, Inc. New York, N.Y.;Gamborg and Phillips (eds) (1995) Plant Cell, Tissue and Organ Culture;Fundamental Methods Springer Lab Manual, Springer-Verlag (BerlinHeidelberg N.Y.); Jones, ed. (1984) Plant Gene Transfer and ExpressionProtocols, Humana Press, Totowa, N.J. and Plant Molecular Biology (1993)R. R. D. Croy, Ed. Bios Scientific Publishers, Oxford, U.K. ISBN 0 12198370 6, all of which are incorporated herein by reference. Cellculture media in general are set forth in Atlas and Parks (eds.) TheHandbook of Microbiological Media (1993) CRC Press, Boca Raton, Fla.,which is incorporated herein by reference. Additional information forcell culture is found in available commercial literature such as theLife Science Research Cell Culture Catalogue from Sigma-Aldrich, Inc (StLouis, Mo.) (“Sigma-LSRCCC”) and, for example, The Plant CultureCatalogue and supplement also from Sigma-Aldrich, Inc (St Louis, Mo.)(“Sigma-PCCS”), all of which are incorporated herein by reference.

The culture medium to be used must in a suitable manner satisfy thedemands of the respective strains. Descriptions of culture media forvarious microorganisms are present in the “Manual of Methods for GeneralBacteriology” of the American Society for Bacteriology (Washington D.C.,USA, 1981).

The present disclosure furthermore provides a process for fermentativepreparation of a product of interest, comprising the steps of: a)culturing a microorganism according to the present disclosure in asuitable medium, resulting in a fermentation broth; and b) concentratingthe product of interest in the fermentation broth of a) and/or in thecells of the microorganism.

In some embodiments, the present disclosure teaches that themicroorganisms produced may be cultured continuously—as described, forexample, in WO 05/021772—or discontinuously in a batch process (batchcultivation) or in a fed-batch or repeated fed-batch process for thepurpose of producing the desired organic-chemical compound. A summary ofa general nature about known cultivation methods is available in thetextbook by Chmiel (Bioprozeßtechnik. 1: Einführung in dieBioverfahrenstechnik (Gustav Fischer Verlag, Stuttgart, 1991)) or in thetextbook by Storhas (Bioreaktoren and periphere Einrichtungen (ViewegVerlag, Braunschweig/Wiesbaden, 1994)).

In some embodiments, the cells of the present disclosure are grown underbatch or continuous fermentations conditions.

Classical batch fermentation is a closed system, wherein thecompositions of the medium is set at the beginning of the fermentationand is not subject to artificial alternations during the fermentation. Avariation of the batch system is a fed-batch fermentation which alsofinds use in the present disclosure. In this variation, the substrate isadded in increments as the fermentation progresses. Fed-batch systemsare useful when catabolite repression is likely to inhibit themetabolism of the cells and where it is desirable to have limitedamounts of substrate in the medium. Batch and fed-batch fermentationsare common and well known in the art.

Continuous fermentation is a system where a defined fermentation mediumis added continuously to a bioreactor and an equal amount of conditionedmedium is removed simultaneously for processing and harvesting ofdesired biomolecule products of interest. In some embodiments,continuous fermentation generally maintains the cultures at a constanthigh density where cells are primarily in log phase growth. In someembodiments, continuous fermentation generally maintains the cultures ata stationary or late log/stationary, phase growth. Continuousfermentation systems strive to maintain steady state growth conditions.

Methods for modulating nutrients and growth factors for continuousfermentation processes as well as techniques for maximizing the rate ofproduct formation are well known in the art of industrial microbiology.

For example, a non-limiting list of carbon sources for the cultures ofthe present disclosure include, sugars and carbohydrates such as, forexample, glucose, sucrose, lactose, fructose, maltose, molasses,sucrose-containing solutions from sugar beet or sugar cane processing,starch, starch hydrolysate, and cellulose; oils and fats such as, forexample, soybean oil, sunflower oil, groundnut oil and coconut fat;fatty acids such as, for example, palmitic acid, stearic acid, andlinoleic acid; alcohols such as, for example, glycerol, methanol, andethanol; and organic acids such as, for example, acetic acid or lacticacid.

A non-limiting list of the nitrogen sources for the cultures of thepresent disclosure include, organic nitrogen-containing compounds suchas peptones, yeast extract, meat extract, malt extract, corn steepliquor, soybean flour, and urea; or inorganic compounds such as ammoniumsulfate, ammonium chloride, ammonium phosphate, ammonium carbonate, andammonium nitrate. The nitrogen sources can be used individually or as amixture.

A non-limiting list of the possible phosphorus sources for the culturesof the present disclosure include, phosphoric acid, potassium dihydrogenphosphate or dipotassium hydrogen phosphate or the correspondingsodium-containing salts.

The culture medium may additionally comprise salts, for example in theform of chlorides or sulfates of metals such as, for example, sodium,potassium, magnesium, calcium and iron, such as, for example, magnesiumsulfate or iron sulfate, which are necessary for growth.

Finally, essential growth factors such as amino acids, for examplehomoserine and vitamins, for example thiamine, biotin or pantothenicacid, may be employed in addition to the abovementioned substances.

In some embodiments, the pH of the culture can be controlled by any acidor base, or buffer salt, including, but not limited to sodium hydroxide,potassium hydroxide, ammonia, or aqueous ammonia; or acidic compoundssuch as phosphoric acid or sulfuric acid in a suitable manner. In someembodiments, the pH is generally adjusted to a value of from 6.0 to 8.5,preferably 6.5 to 8.

In some embodiments, the cultures of the present disclosure may includean anti-foaming agent such as, for example, fatty acid polyglycolesters. In some embodiments the cultures of the present disclosure aremodified to stabilize the plasmids of the cultures by adding suitableselective substances such as, for example, antibiotics.

In some embodiments, the culture is carried out under aerobicconditions. In order to maintain these conditions, oxygen oroxygen-containing gas mixtures such as, for example, air are introducedinto the culture. It is likewise possible to use liquids enriched withhydrogen peroxide. The fermentation is carried out, where appropriate,at elevated pressure, for example at an elevated pressure of from 0.03to 0.2 MPa. The temperature of the culture is normally from 20° C. to45° C. and preferably from 25° C. to 40° C., particularly preferablyfrom 30° C. to 37° C. In batch or fed-batch processes, the cultivationis preferably continued until an amount of the desired product ofinterest (e.g. an organic-chemical compound) sufficient for beingrecovered has formed. This aim can normally be achieved within 10 hoursto 160 hours. In continuous processes, longer cultivation times arepossible. The activity of the microorganisms results in a concentration(accumulation) of the product of interest in the fermentation mediumand/or in the cells of said microorganisms.

In some embodiments, the culture is carried out under anaerobicconditions.

Screening

In some embodiments, the present disclosure teaches high-throughputinitial screenings. In other embodiments, the present disclosure alsoteaches robust tank-based validations of performance data (see FIG. 6B).

In some embodiments, the high-throughput screening process is designedto predict performance of strains in bioreactors. As previouslydescribed, culture conditions are selected to be suitable for theorganism and reflective of bioreactor conditions. Individual coloniesare picked and transferred into 96 well plates and incubated for asuitable amount of time. Cells are subsequently transferred to new 96well plates for additional seed cultures, or to production cultures.Cultures are incubated for varying lengths of time, where multiplemeasurements may be made. These may include measurements of product,biomass or other characteristics that predict performance of strains inbioreactors. High-throughput culture results are used to predictbioreactor performance.

In some embodiments, the tank-based performance validation is used toconfirm performance of strains isolated by high throughput screening.Fermentation processes/conditions are obtained from client sites.Candidate strains are screened using bench scale fermentation reactors(e.g., reactors disclosed in Table 5 of the present disclosure) forrelevant strain performance characteristics such as productivity oryield.

Product Recovery and Quantification

Methods for screening for the production of products of interest areknown to those of skill in the art and are discussed throughout thepresent specification. Such methods may be employed when screening thestrains of the disclosure.

In some embodiments, the present disclosure teaches methods of improvingstrains designed to produce non-secreted intracellular products. Forexample, the present disclosure teaches methods of improving therobustness, yield, efficiency, or overall desirability of cell culturesproducing intracellular enzymes, oils, pharmaceuticals, or othervaluable small molecules or peptides. The recovery or isolation ofnon-secreted intracellular products can be achieved by lysis andrecovery techniques that are well known in the art, including thosedescribed herein.

For example, in some embodiments, cells of the present disclosure can beharvested by centrifugation, filtration, settling, or other method.Harvested cells are then disrupted by any convenient method, includingfreeze-thaw cycling, sonication, mechanical disruption, or use of celllysing agents, or other methods, which are well known to those skilledin the art.

The resulting product of interest, e.g. a polypeptide, may berecovered/isolated and optionally purified by any of a number of methodsknown in the art. For example, a product polypeptide may be isolatedfrom the nutrient medium by conventional procedures including, but notlimited to: centrifugation, filtration, extraction, spray-drying,evaporation, chromatography (e.g., ion exchange, affinity, hydrophobicinteraction, chromatofocusing, and size exclusion), or precipitation.Finally, high performance liquid chromatography (HPLC) can be employedin the final purification steps. (See for example Purification ofintracellular protein as described in Parry et al., 2001, Biochem.1353:117, and Hong et al., 2007, Appl. Microbiol. Biotechnol. 73:1331,both incorporated herein by reference).

In addition to the references noted supra, a variety of purificationmethods are well known in the art, including, for example, those setforth in: Sandana (1997) Bioseparation of Proteins, Academic Press,Inc.; Bollag et al. (1996) Protein Methods, 2^(nd) Edition, Wiley-Liss,NY; Walker (1996) The Protein Protocols Handbook Humana Press, NJ;Harris and Angal (1990) Protein Purification Applications: A PracticalApproach, IRL Press at Oxford, Oxford, England; Harris and Angal ProteinPurification Methods: A Practical Approach, IRL Press at Oxford, Oxford,England; Scopes (1993) Protein Purification: Principles and Practice3^(rd) Edition, Springer Verlag, NY; Janson and Ryden (1998) ProteinPurification: Principles, High Resolution Methods and Applications,Second Edition, Wiley-VCH, NY; and Walker (1998) Protein Protocols onCD-ROM, Humana Press, NJ, all of which are incorporated herein byreference.

In some embodiments, the present disclosure teaches the methods ofimproving strains designed to produce secreted products. For example,the present disclosure teaches methods of improving the robustness,yield, efficiency, or overall desirability of cell cultures producingvaluable small molecules or peptides.

In some embodiments, immunological methods may be used to detect and/orpurify secreted or non-secreted products produced by the cells of thepresent disclosure. In one example approach, antibody raised against aproduct molecule (e.g., against an insulin polypeptide or an immunogenicfragment thereof) using conventional methods is immobilized on beads,mixed with cell culture media under conditions in which theendoglucanase is bound, and precipitated. In some embodiments, thepresent disclosure teaches the use of enzyme-linked immunosorbent assays(ELISA).

In other related embodiments, immunochromatography is used, as disclosedin U.S. Pat. Nos. 5,591,645, 4,855,240, 4,435,504, 4,980,298, andSe-Hwan Paek, et al., “Development of rapid One-StepImmunochromatographic assay, Methods”, 22, 53-60, 2000), each of whichare incorporated by reference herein. A general immunochromatographydetects a specimen by using two antibodies. A first antibody exists in atest solution or at a portion at an end of a test piece in anapproximately rectangular shape made from a porous membrane, where thetest solution is dropped. This antibody is labeled with latex particlesor gold colloidal particles (this antibody will be called as a labeledantibody hereinafter). When the dropped test solution includes aspecimen to be detected, the labeled antibody recognizes the specimen soas to be bonded with the specimen. A complex of the specimen and labeledantibody flows by capillarity toward an absorber, which is made from afilter paper and attached to an end opposite to the end having includedthe labeled antibody. During the flow, the complex of the specimen andlabeled antibody is recognized and caught by a second antibody (it willbe called as a tapping antibody hereinafter) existing at the middle ofthe porous membrane and, as a result of this, the complex appears at adetection part on the porous membrane as a visible signal and isdetected.

In some embodiments, the screening methods of the present disclosure arebased on photometric detection techniques (absorption, fluorescence).For example, in some embodiments, detection may be based on the presenceof a fluorophore detector such as GFP bound to an antibody. In otherembodiments, the photometric detection may be based on the accumulationon the desired product from the cell culture. In some embodiments, theproduct may be detectable via UV of the culture or extracts from saidculture.

Persons having skill in the art will recognize that the methods of thepresent disclosure are compatible with host cells producing anydesirable biomolecule product of interest. Table 2 below presents anon-limiting list of the product categories, biomolecules, and hostcells, included within the scope of the present disclosure. Theseexamples are provided for illustrative purposes, and are not meant tolimit the applicability of the presently disclosed technology in anyway.

TABLE 2 A non-limiting list of the host cells and products of interestof the present disclosure. Product category Products Host category HostsAmino acids Lysine Bacteria Corynebacterium glutamicum Amino acidsMethionine Bacteria Escherichia coli Amino acids MSG BacteriaCorynebacterium glutamicum Amino acids Threonine Bacteria Escherichiacoli Amino acids Threonine Bacteria Corynebacterium glutamicum Aminoacids Tryptophan Bacteria Corynebacterium glutamicum Enzymes Enzymes(11) Filamentous fungi Trichoderma reesei Enzymes Enzymes (11) FungiMyceliopthora thermophila (C1) Enzymes Enzymes (11) Filamentous fungiAspergillus oryzae Enzymes Enzymes (11) Filamentous fungi Aspergillusniger Enzymes Enzymes (11) Bacteria Bacillus subtilis Enzymes Enzymes(11) Bacteria Bacillus licheniformis Enzymes Enzymes (11) BacteriaBacillus clausii Flavor & Agarwood Yeast Saccharomyces cerevisiaeFragrance Flavor & Ambrox Yeast Saccharomyces cerevisiae FragranceFlavor & Nootkatone Yeast Saccharomyces cerevisiae Fragrance Flavor &Patchouli oil Yeast Saccharomyces cerevisiae Fragrance Flavor & SaffronYeast Saccharomyces cerevisiae Fragrance Flavor & Sandalwood oil YeastSaccharomyces cerevisiae Fragrance Flavor & Valencene YeastSaccharomyces cerevisiae Fragrance Flavor & Vanillin Yeast Saccharomycescerevisiae Fragrance Food CoQ10/Ubiquinol Yeast Schizosaccharomycespombe Food Omega 3 fatty Microalgae Schizochytrium acids Food Omega 6fatty Microalgae Schizochytrium acids Food Vitamin B12 BacteriaPropionibacterium freudenreichii Food Vitamin B2 Filamentous fungiAshbya gossypii Food Vitamin B2 Bacteria Bacillus subtilis FoodErythritol Yeast-like fungi Torula coralline Food Erythritol Yeast-likefungi Pseudozyma tsukubaensis Food Erythritol Yeast-like fungiMoniliella pollinis Food Steviol Yeast Saccharomyces cerevisiaeglycosides Hydrocolloids Diutan gum Bacteria Sphingomonas spHydrocolloids Gellan gum Bacteria Sphingomonas elodea HydrocolloidsXanthan gum Bacteria Xanthomonas campestris Intermediates 1,3-PDOBacteria Escherichia coli Intermediates 1,4-BDO Bacteria Escherichiacoli Intermediates Butadiene Bacteria Cupriavidus necator Intermediatesn-butanol Bacteria (obligate Clostridium acetobutylicum anaerobe)Organic acids Citric acid Filamentous fungi Aspergillus niger Organicacids Citric acid Yeast Pichia guilliermondii Organic acids Gluconicacid Filamentous fungi Aspergillus niger Organic acids Itaconic acidFilamentous fungi Aspergillus terreus Organic acids Lactic acid BacteriaLactobacillus Organic acids Lactic acid Bacteria Geobacillusthermoglucosidasius Organic acids LCDAs-DDDA Yeast CandidaPolyketides/Ag Spinosad Yeast Saccharopolyspora spinosa Polyketides/AgSpinetoram Yeast Saccharopolyspora spinosa

Selection Criteria and Goals

The selection criteria applied to the methods of the present disclosurewill vary with the specific goals of the strain improvement program. Thepresent disclosure may be adapted to meet any program goals. Forexample, in some embodiments, the program goal may be to maximize singlebatch yields of reactions with no immediate time limits. In otherembodiments, the program goal may be to rebalance biosynthetic yields toproduce a specific product, or to produce a particular ratio ofproducts. In other embodiments, the program goal may be to modify thechemical structure of a product, such as lengthening the carbon chain ofa polymer. In some embodiments, the program goal may be to improveperformance characteristics such as yield, titer, productivity,by-product elimination, tolerance to process excursions, optimal growthtemperature and growth rate. In some embodiments, the program goal isimproved host performance as measured by volumetric productivity,specific productivity, yield or titre, of a product of interest producedby a microbe.

In other embodiments, the program goal may be to optimize synthesisefficiency of a commercial strain in terms of final product yield perquantity of inputs (e.g., total amount of ethanol produced per pound ofsucrose). In other embodiments, the program goal may be to optimizesynthesis speed, as measured for example in terms of batch completionrates, or yield rates in continuous culturing systems. In otherembodiments, the program goal may be to increase strain resistance to aparticular phage, or otherwise increase strain vigor/robustness underculture conditions.

In some embodiments, strain improvement projects may be subject to morethan one goal. In some embodiments, the goal of the strain project mayhinge on quality, reliability, or overall profitability. In someembodiments, the present disclosure teaches methods of associatedselected mutations or groups of mutations with one or more of the strainproperties described above.

Persons having ordinary skill in the art will recognize how to tailorstrain selection criteria to meet the particular project goal. Forexample, selections of a strain's single batch max yield at reactionsaturation may be appropriate for identifying strains with high singlebatch yields. Selection based on consistency in yield across a range oftemperatures and conditions may be appropriate for identifying strainswith increased robustness and reliability.

In some embodiments, the selection criteria for the initialhigh-throughput phase and the tank-based validation will be identical.In other embodiments, tank-based selection may operate under additionaland/or different selection criteria. For example, in some embodiments,high-throughput strain selection might be based on single batch reactioncompletion yields, while tank-based selection may be expanded to includeselections based on yields for reaction speed.

Sequencing

In some embodiments, the present disclosure teaches whole-genomesequencing of the organisms described herein. In other embodiments, thepresent disclosure also teaches sequencing of plasmids, PCR products,and other oligos as quality controls to the methods of the presentdisclosure. Sequencing methods for large and small projects are wellknown to those in the art.

In some embodiments, any high-throughput technique for sequencingnucleic acids can be used in the methods of the disclosure. In someembodiments, the present disclosure teaches whole genome sequencing. Inother embodiments, the present disclosure teaches amplicon sequencingultra deep sequencing to identify genetic variations. In someembodiments, the present disclosure also teaches novel methods forlibrary preparation, including tagmentation (see WO/2016/073690). DNAsequencing techniques include classic dideoxy sequencing reactions(Sanger method) using labeled terminators or primers and gel separationin slab or capillary; sequencing by synthesis using reversiblyterminated labeled nucleotides, pyrosequencing; 454 sequencing; allelespecific hybridization to a library of labeled oligonucleotide probes;sequencing by synthesis using allele specific hybridization to a libraryof labeled clones that is followed by ligation; real time monitoring ofthe incorporation of labeled nucleotides during a polymerization step;polony sequencing; and SOLiD sequencing.

In one aspect of the disclosure, high-throughput methods of sequencingare employed that comprise a step of spatially isolating individualmolecules on a solid surface where they are sequenced in parallel. Suchsolid surfaces may include nonporous surfaces (such as in Solexasequencing, e.g. Bentley et al, Nature, 456: 53-59 (2008) or CompleteGenomics sequencing, e.g. Drmanac et al, Science, 327: 78-81 (2010)),arrays of wells, which may include bead- or particle-bound templates(such as with 454, e.g. Margulies et al, Nature, 437: 376-380 (2005) orIon Torrent sequencing, U.S. patent publication 2010/0137143 or2010/0304982), micromachined membranes (such as with SMRT sequencing,e.g. Eid et al, Science, 323: 133-138 (2009)), or bead arrays (as withSOLiD sequencing or polony sequencing, e.g. Kim et al, Science, 316:1481-1414 (2007)).

In another embodiment, the methods of the present disclosure compriseamplifying the isolated molecules either before or after they arespatially isolated on a solid surface. Prior amplification may compriseemulsion-based amplification, such as emulsion PCR, or rolling circleamplification. Also taught is Solexa-based sequencing where individualtemplate molecules are spatially isolated on a solid surface, afterwhich they are amplified in parallel by bridge PCR to form separateclonal populations, or clusters, and then sequenced, as described inBentley et al (cited above) and in manufacturer's instructions (e.g.TruSeq™ Sample Preparation Kit and Data Sheet, Illumina, Inc., SanDiego, Calif., 2010); and further in the following references: U.S. Pat.Nos. 6,090,592; 6,300,070; 7,115,400; and EP0972081B1; which areincorporated by reference.

In one embodiment, individual molecules disposed and amplified on asolid surface form clusters in a density of at least 10⁵ clusters percm²; or in a density of at least 5×10⁵ per cm²; or in a density of atleast 10⁶ clusters per cm². In one embodiment, sequencing chemistriesare employed having relatively high error rates. In such embodiments,the average quality scores produced by such chemistries aremonotonically declining functions of sequence read lengths. In oneembodiment, such decline corresponds to 0.5 percent of sequence readshave at least one error in positions 1-75; 1 percent of sequence readshave at least one error in positions 76-100; and 2 percent of sequencereads have at least one error in positions 101-125.

Computational Analysis and Prediction of Effects of Genome-Wide GeneticDesign Criteria

In some embodiments, the present disclosure teaches methods ofpredicting the effects of particular genetic alterations beingincorporated into a given host strain. In further aspects, thedisclosure provides methods for generating proposed genetic alterationsthat should be incorporated into a given host strain, in order for saidhost to possess a particular phenotypic trait or strain parameter. Ingiven aspects, the disclosure provides predictive models that can beutilized to design novel host strains.

In some embodiments, the present disclosure teaches methods of analyzingthe performance results of each round of screening and methods forgenerating new proposed genome-wide sequence modifications predicted toenhance strain performance in the following round of screening.

In some embodiments, the present disclosure teaches that the systemgenerates proposed sequence modifications to host strains based onprevious screening results. In some embodiments, the recommendations ofthe present system are based on the results from the immediatelypreceding screening. In other embodiments, the recommendations of thepresent system are based on the cumulative results of one or more of thepreceding screenings.

In some embodiments, the recommendations of the present system are basedon previously developed HTP genetic design libraries. For example, insome embodiments, the present system is designed to save results fromprevious screenings, and apply those results to a different project, inthe same or different host organisms.

In other embodiments, the recommendations of the present system arebased on scientific insights. For example, in some embodiments, therecommendations are based on known properties of genes (from sourcessuch as annotated gene databases and the relevant literature), codonoptimization, transcriptional slippage, uORFs, or other hypothesisdriven sequence and host optimizations.

In some embodiments, the proposed sequence modifications to a hoststrain recommended by the system, or predictive model, are carried outby the utilization of one or more of the disclosed molecular tools setscomprising: (1) Promoter swaps, (2) SNP swaps, (3) Start/Stop codonexchanges, (4) Sequence optimization, (5) Stop swaps, and (5) Epistasismapping.

The HTP genetic engineering platform described herein is agnostic withrespect to any particular microbe or phenotypic trait (e.g. productionof a particular compound). That is, the platform and methods taughtherein can be utilized with any host cell to engineer said host cell tohave any desired phenotypic trait. Furthermore, the lessons learned froma given HTP genetic engineering process used to create one novel hostcell, can be applied to any number of other host cells, as a result ofthe storage, characterization, and analysis of a myriad of processparameters that occurs during the taught methods.

As alluded to in the epistatic mapping section, it is possible toestimate the performance (a.k.a. score) of a hypothetical strainobtained by consolidating a collection of mutations from a HTP geneticdesign library into a particular background via some preferredpredictive model. Given such a predictive model, it is possible to scoreand rank all hypothetical strains accessible to the mutation library viacombinatorial consolidation. The below section outlines particularmodels utilized in the present HTP platform.

Predictive Strain Design

Described herein is an approach for predictive strain design, including:methods of describing genetic changes and strain performance, predictingstrain performance based on the composition of changes in the strain,recommending candidate designs with high predicted performance, andfiltering predictions to optimize for second-order considerations, e.g.similarity to existing strains, epistasis, or confidence in predictions.

Inputs to Strain Design Model

In one embodiment, for the sake of ease of illustration, input data maycomprise two components: (1) sets of genetic changes and (2) relativestrain performance. Those skilled in the art will recognize that thismodel can be readily extended to consider a wide variety of inputs,while keeping in mind the countervailing consideration of overfitting.In addition to genetic changes, some of the input parameters(independent variables) that can be adjusted are cell types (genus,species, strain, phylogenetic characterization, etc.) and processparameters (e.g., environmental conditions, handling equipment,modification techniques, etc.) under which fermentation is conductedwith the cells.

The sets of genetic changes can come from the previously discussedcollections of genetic perturbations termed HTP genetic designlibraries. The relative strain performance can be assessed based uponany given parameter or phenotypic trait of interest (e.g. production ofa compound, small molecule, or product of interest).

Cell types can be specified in general categories such as prokaryoticand eukaryotic systems, genus, species, strain, tissue cultures (vs.disperse cells), etc. Process parameters that can be adjusted includetemperature, pressure, reactor configuration, and medium composition.Examples of reactor configuration include the volume of the reactor,whether the process is a batch or continuous, and, if continuous, thevolumetric flow rate, etc. One can also specify the support structure,if any, on which the cells reside. Examples of medium compositioninclude the concentrations of electrolytes, nutrients, waste products,acids, pH, and the like.

Sets of Genetic Changes from Selected HTP Genetic Design Libraries to beUtilized in the Initial Linear Regression Model that Subsequently isUsed to Create the Predictive Strain Design Model

An example set of entries from a table of genetic changes is shown belowin Table 3. Each row indicates a genetic change in strain 7000051473, aswell as metadata about the mechanism of change, e.g. promoter swap orSNP swap. aceE, zwf, and pyc are all related to the citric acid cycle.

In this case strain 7000051473 has a total of 7 changes. “Last change”means the change in this strain represents the most recent modificationin this strain lineage. Thus, comparing this strain's performance to theperformance of its parent represents a data point concerning theperformance of the “last change” mutation.

TABLE 3 Strain design entry table for strain 7000051473 strain namelibrary change from to last_change 7000051473 dlc19_42 proswp pcg3121cg1144 pcg3121_cg1144 1 7000051473 dlc19_42 scswp acee atg > ttg ttgacee_atg 0 7000051473 dlc19_42 snpswp dss_033 NA na 0 7000051473dlc19_42 snpswp dss_084 NA t 0 7000051473 dlc19_42 snpswp dss_316 NA na0 7000051473 dlc19_42 proswp pcg0007_39 zwf pcg0007_39_zwf 0 7000051473dlc19_42 proswp pcg1860 pyc pcg1860_pyc 0

Built Strain Performance Assessment

The goal of the taught model is to predict strain performance based onthe composition of genetic changes introduced to the strain. Toconstruct a standard for comparison, strain performance is computedrelative to a common reference strain, by first calculating the medianperformance per strain, per assay plate. Relative performance is thencomputed as the difference in average performance between an engineeredstrain and the common reference strain within the same plate.Restricting the calculations to within-plate comparisons ensures thatthe samples under consideration all received the same experimentalconditions.

FIG. 23 shows the distribution of relative strain performances for theinput data under consideration. A relative performance of zero indicatesthat the engineered strain performed equally well to the in-plate baseor “reference” strain. Of interest is the ability of the predictivemodel to identify the strains that are likely to perform significantlyabove zero. Further, and more generally, of interest is whether anygiven strain outperforms its parent by some criteria. In practice, thecriteria can be a product titer meeting or exceeding some thresholdabove the parent level, though having a statistically significantdifference from the parent in the desired direction could also be usedinstead or in addition. The role of the base or “reference” strain issimply to serve as an added normalization factor for making comparisonswithin or between plates.

A concept to keep in mind is that of differences between: parent strainand reference strain. The parent strain is the background that was usedfor a current round of mutagenesis. The reference strain is a controlstrain run in every plate to facilitate comparisons, especially betweenplates, and is typically the “base strain” as referenced above. Butsince the base strain (e.g., the wild-type or industrial strain beingused to benchmark overall performance) is not necessarily a “base” inthe sense of being a mutagenesis target in a given round of strainimprovement, a more descriptive term is “reference strain.”

In summary, a base/reference strain is used to benchmark the performanceof built strains, generally, while the parent strain is used tobenchmark the performance of a specific genetic change in the relevantgenetic background.

Ranking the Performance of Built Strains with Linear Regression

The goal of the disclosed model is to rank the performance of builtstrains, by describing relative strain performance, as a function of thecomposition of genetic changes introduced into the built strains. Asdiscussed throughout the disclosure, the various HTP genetic designlibraries provide the repertoire of possible genetic changes (e.g.,genetic perturbations/alterations) that are introduced into theengineered strains. Linear regression is the basis for the currentlydescribed exemplary predictive model.

The below table contains example input for regression-based modeling.The strain performances are ranked relative to a common base strain, asa function of the composition of the genetic changes contained in thestrain.

Each column heading represents a genetic change, a “1” represents thepresence of the change, whereas a “0” represents the absence of achange. “DSS” refers to SNP swaps from a particular library (first 3columns after relative_perf). The last 3 columns are promoter swaps,where the pcgXXXX denotes the particular promoter, and the last 3letters represent the gene the promoter is being applied to. The genesare related to central metabolism. The promoters are fromCorynebacterium glutamicum (hence the “cg” notation). Furtherinformation on the utilized promoters can be found in Table 1, listingpromoters P1-P8, and the sequence listing of the present application.Further, detailed information on each promoter P1-P8 can be found inU.S. Provisional Application No. 62/264,232, filed on Dec. 7, 2015, andentitled “Promoters from Corynebacterium glutamicum,” which isincorporated herein by reference. For ease of reference, in the belowtable, pcg3121=P8; pcg0755=P4; and pcg1860=P3.

TABLE 4 Summary of genetic changes and their effect on relativeperformance. relative_perf dss_033 dss_034 dss_056 pcg3121_pgipcg0755_zwf pcg1860_pyc 0.1358908 0 0 0 0 0 1 −1.8946985 1 0 0 1 0 1−0.0222045 0 0 0 1 0 0 0.6342183 1 0 1 0 0 0 −0.0803285 1 1 0 0 0 02.6468117 0 0 0 1 0 0Linear Regression to Characterize Built Strains

Linear regression is an attractive method for the described HTP genomicengineering platform, because of the ease of implementation andinterpretation. The resulting regression coefficients can be interpretedas the average increase or decrease in relative strain performanceattributable to the presence of each genetic change.

For example, as seen in FIG. 24, this technique allows us to concludethat changing the pgi promoter to pcg3121 improves relative strainperformance by approximately 5 units on average and is thus apotentially highly desirable change, in the absence of any negativeepistatic interactions (note: the input is a unit-less normalizedvalue).

The taught method therefore uses linear regression models todescribe/characterize and rank built strains, which have various geneticperturbations introduced into their genomes from the various taughtlibraries.

Predictive Design Modeling

The linear regression model described above, which utilized data fromconstructed strains, can be used to make performance predictions forstrains that haven't yet been built.

The procedure can be summarized as follows: generate in silico allpossible configurations of genetic changes→use the regression model topredict relative strain performance→order the candidate strain designsby performance. Thus, by utilizing the regression model to predict theperformance of as-yet-unbuilt strains, the method allows for theproduction of higher performing strains, while simultaneously conductingfewer experiments.

Generate Configurations

When constructing a model to predict performance of as-yet-unbuiltstrains, the first step is to produce a sequence of design candidates.This is done by fixing the total number of genetic changes in thestrain, and then defining all possible combinations of genetic changes.For example, one can set the total number of potential geneticchanges/perturbations to 29 (e.g. 29 possible SNPs, or 29 differentpromoters, or any combination thereof as long as the universe of geneticperturbations is 29) and then decide to design all possible 3-membercombinations of the 29 potential genetic changes, which will result in3,654 candidate strain designs.

To provide context to the aforementioned 3,654 candidate strains,consider that one can calculate the number of non-redundant groupings ofsize r from n possible members using n!/((n−r)!*r!). If r=3, n=29 gives3,654. Thus, if one designs all possible 3-member combinations of 29potential changes the results is 3,654 candidate strains. The 29potential genetic changes are present in the x-axis of FIG. 25.

Predict Performance of New Strain Designs

Using the linear regression constructed above with the combinatorialconfigurations as input, one can then predict the expected relativeperformance of each candidate design. FIG. 25 summarizes the compositionof changes for the top 100 predicted strain designs. The x-axis liststhe pool of potential genetic changes (29 possible genetic changes), andthe y-axis shows the rank order. Black cells indicate the presence of aparticular change in the candidate design, while white cells indicatethe absence of that change. In this particular example, all of the top100 designs contain the changes pcg3121_pgi, pcg1860_pyc, dss_339, andpcg0007_39_lysa. Additionally, the top candidate design contains thechanges dss_034, dss_009.

Predictive accuracy should increase over time as new observations areused to iteratively retrain and refit the model. Results from a study bythe inventors illustrate the methods by which the predictive model canbe iteratively retrained and improved. FIG. 47 compares modelpredictions with observed measurement values. The quality of modelpredictions can be assessed through several methods, including acorrelation coefficient indicating the strength of association betweenthe predicted and observed values, or the root-mean-square error, whichis a measure of the average model error. Using a chosen metric for modelevaluation, the system may define rules for when the model should beretrained.

A couple of unstated assumptions to the above model include: (1) thereare no epistatic interactions; and (2) the genetic changes/perturbationsutilized to build the predictive model (e.g. from built strain data asillustrated in FIG. 24, or whatever data set is used as the reference toconstruct the model) were all made in the same background, as theproposed combinations of genetic changes (e.g. as illustrated in FIG.25).

Filtering for Second-Order Features

The above illustrative example focused on linear regression predictionsbased on predicted host cell performance. In some embodiments, thepresent linear regression methods can also be applied to non-biomoleculefactors, such as saturation biomass, resistance, or other measurablehost cell features. Thus the methods of the present disclosure alsoteach in considering other features outside of predicted performancewhen prioritizing the candidates to build. Assuming there is additionalrelevant data, nonlinear terms are also included in the regressionmodel.

Closeness with Existing Strains

Predicted strains that are similar to ones that have already been builtcould result in time and cost savings despite not being a top predictedcandidate

Diversity of Changes

When constructing the aforementioned models, one cannot be certain thatgenetic changes will truly be additive (as assumed by linear regressionand mentioned as an assumption above) due to the presence of epistaticinteractions. Therefore, knowledge of genetic change dissimilarity canbe used to increase the likelihood of positive additivity. If one knows,for example, that the changes dss_034 and dss_009 (which are SNP swaps)from the top ranked strain above are on the same metabolic pathway andhave similar performance characteristics, then that information could beused to select another top ranking strain with a dissimilar compositionof changes. As described in the section above concerning epistasismapping, the predicted best genetic changes may be filtered to restrictselection to mutations with sufficiently dissimilar response profiles.Alternatively, the linear regression may be a weighted least squaresregression using the similarity matrix to weight predictions.

Diversity of Predicted Performance

Finally, one may choose to design strains with middling or poorpredicted performance, in order to validate and subsequently improve thepredictive models.

Iterative Strain Design Optimization

As described for the example above, all of the top 100 strain designscontain the changes pcg3121_pgi, pcg1860_pyc, dss_339, andpcg0007_39_lysa. Additionally, the top candidate strain design containsthe changes dss_034, dss_009.

In embodiments, the order placement engine 208 places a factory order tothe factory 210 to manufacture microbial strains incorporating the topcandidate mutations. In feedback-loop fashion, the results may beanalyzed by the analysis equipment 214 to determine which microbesexhibit desired phenotypic properties (314). During the analysis phase,the modified strain cultures are evaluated to determine theirperformance, i.e., their expression of desired phenotypic properties,including the ability to be produced at industrial scale. For example,the analysis phase uses, among other things, image data of plates tomeasure microbial colony growth as an indicator of colony health. Theanalysis equipment 214 is used to correlate genetic changes withphenotypic performance, and save the resulting genotype-phenotypecorrelation data in libraries, which may be stored in library 206, toinform future microbial production.

In particular, the candidate changes that actually result insufficiently high measured performance may be added as rows in thedatabase to tables such as Table 4 above. In this manner, the bestperforming mutations are added to the predictive strain design model ina supervised machine learning fashion.

LIMS iterates the design/build/test/analyze cycle based on thecorrelations developed from previous factory runs. During a subsequentcycle, the analysis equipment 214 alone, or in conjunction with humanoperators, may select the best candidates as base strains for input backinto input interface 202, using the correlation data to fine tunegenetic modifications to achieve better phenotypic performance withfiner granularity. In this manner, the laboratory information managementsystem of embodiments of the disclosure implements a quality improvementfeedback loop.

In sum, with reference to the flowchart of FIG. 33 the iterativepredictive strain design workflow may be described as follows:

-   -   Generate a training set of input and output variables, e.g.,        genetic changes as inputs and performance features as outputs        (3302). Generation may be performed by the analysis equipment        214 based upon previous genetic changes and the corresponding        measured performance of the microbial strains incorporating        those genetic changes.    -   Develop an initial model (e.g., linear regression model) based        upon training set (3304). This may be performed by the analysis        equipment 214.    -   Generate design candidate strains (3306)        -   In one embodiment, the analysis equipment 214 may fix the            number of genetic changes to be made to a background strain,            in the form of combinations of changes. To represent these            changes, the analysis equipment 214 may provide to the            interpreter 204 one or more DNA specification expressions            representing those combinations of changes. (These genetic            changes or the microbial strains incorporating those changes            may be referred to as “test inputs.”) The interpreter 204            interprets the one or more DNA specifications, and the            execution engine 207 executes the DNA specifications to            populate the DNA specification with resolved outputs            representing the individual candidate design strains for            those changes.    -   Based upon the model, the analysis equipment 214 predicts        expected performance of each candidate design strain (3308).    -   The analysis equipment 214 selects a limited number of candidate        designs, e.g., 100, with highest predicted performance (3310).        -   As described elsewhere herein with respect to epistasis            mapping, the analysis equipment 214 may account for            second-order effects such as epistasis, by, e.g., filtering            top designs for epistatic effects, or factoring epistasis            into the predictive model.    -   Build the filtered candidate strains (at the factory 210) based        on the factory order generated by the order placement engine 208        (3312).    -   The analysis equipment 214 measures the actual performance of        the selected strains, selects a limited number of those selected        strains based upon their superior actual performance (3314), and        adds the design changes and their resulting performance to the        predictive model (3316). In the linear regression example, add        the sets of design changes and their associated performance as        new rows in Table 4.    -   The analysis equipment 214 then iterates back to generation of        new design candidate strains (3306), and continues iterating        until a stop condition is satisfied. The stop condition may        comprise, for example, the measured performance of at least one        microbial strain satisfying a performance metric, such as yield,        growth rate, or titer.

In the example above, the iterative optimization of strain designemploys feedback and linear regression to implement machine learning. Ingeneral, machine learning may be described as the optimization ofperformance criteria, e.g., parameters, techniques or other features, inthe performance of an informational task (such as classification orregression) using a limited number of examples of labeled data, and thenperforming the same task on unknown data. In supervised machine learningsuch as that of the linear regression example above, the machine (e.g.,a computing device) learns, for example, by identifying patterns,categories, statistical relationships, or other attributes, exhibited bytraining data. The result of the learning is then used to predictwhether new data will exhibit the same patterns, categories, statisticalrelationships or other attributes.

Embodiments of the disclosure may employ other supervised machinelearning techniques when training data is available. In the absence oftraining data, embodiments may employ unsupervised machine learning.Alternatively, embodiments may employ semi-supervised machine learning,using a small amount of labeled data and a large amount of unlabeleddata. Embodiments may also employ feature selection to select the subsetof the most relevant features to optimize performance of the machinelearning model. Depending upon the type of machine learning approachselected, as alternatives or in addition to linear regression,embodiments may employ for example, logistic regression, neuralnetworks, support vector machines (SVMs), decision trees, hidden Markovmodels, Bayesian networks, Gram Schmidt, reinforcement-based learning,cluster-based learning including hierarchical clustering, geneticalgorithms, and any other suitable learning machines known in the art.In particular, embodiments may employ logistic regression to provideprobabilities of classification (e.g., classification of genes intodifferent functional groups) along with the classifications themselves.See, e.g., Shevade, A simple and efficient algorithm for gene selectionusing sparse logistic regression, Bioinformatics, Vol. 19, No. 17 2003,pp. 2246-2253, Leng, et al., Classification using functional dataanalysis for temporal gene expression data, Bioinformatics, Vol. 22, No.1, Oxford University Press (2006), pp. 68-76, all of which areincorporated by reference in their entirety herein.

Embodiments may employ graphics processing unit (GPU) acceleratedarchitectures that have found increasing popularity in performingmachine learning tasks, particularly in the form known as deep neuralnetworks (DNN). Embodiments of the disclosure may employ GPU-basedmachine learning, such as that described in GPU-Based Deep LearningInference: A Performance and Power Analysis, NVidia Whitepaper, November2015, Dahl, et al., Multi-task Neural Networks for QSAR Predictions,Dept. of Computer Science, Univ. of Toronto, June 2014 (arXiv:1406.1231[stat.ML]), all of which are incorporated by reference in their entiretyherein. Machine learning techniques applicable to embodiments of thedisclosure may also be found in, among other references, Libbrecht, etal., Machine learning applications in genetics and genomics, NatureReviews: Genetics, Vol. 16, June 2015, Kashyap, et al., Big DataAnalytics in Bioinformatics: A Machine Learning Perspective, Journal ofLatex Class Files, Vol. 13, No. 9, Sep. 2014, Prompramote, et al.,Machine Learning in Bioinformatics, Chapter 5 of BioinformaticsTechnologies, pp. 117-153, Springer Berlin Heidelberg 2005, all of whichare incorporated by reference in their entirety herein.

Iterative Predictive Strain Design: Example

The following provides an example application of the iterativepredictive strain design workflow outlined above.

An initial set of training inputs and output variables was prepared.This set comprised 1864 unique engineered strains with defined geneticcomposition. Each strain contained between 5 and 15 engineered changes.A total of 336 unique genetic changes were present in the training.

An initial predictive computer model was developed. The implementationused a generalized linear model (Kernel Ridge Regression with 4th orderpolynomial kernel). The implementation models two distinct phenotypes(yield and productivity). These phenotypes were combined as weighted sumto obtain a single score for ranking, as shown below. Various modelparameters, e.g. regularization factor, were tuned via k-fold crossvalidation over the designated training data.

The implementation does not incorporate any explicit analysis ofinteraction effects as described in the Epistasis Mapping section above.However, as those skilled in the art would understand, the implementedgeneralized linear model may capture interaction effects implicitlythrough the second, third and fourth order terms of the kernel.

The model was trained against the training set. The fitted model has anR² value (coefficient of determination) of 0.52 with respect to yieldand an R² value of 0.67 with respect to productivity. FIG. 47demonstrates a significant quality fitting of the yield model to thetraining data.

Candidate strains were generated. This example includes a serial buildconstraint associated with the introduction of new genetic changes to aparent strain (in this example, only one new mutation was engineeredinto a strain at a time). Here, candidates are not considered simply asa function of the desired number of changes. Instead, the analysisequipment 214 selected, as a starting point, a collection of previouslydesigned strains known to have high performance metrics (“seedstrains”). The analysis equipment 214 individually applied geneticchanges to each of the seed strains. The introduced genetic changes didnot include those already present in the seed strain. For varioustechnical, biological or other reasons, certain mutations wereexplicitly required, e.g., opca_4, or explicitly excluded, e.g.,dss_422. Using 166 available seed strains and the 336 changescharacterized by the model, 6239 novel candidate strains were designed.

Based upon the model, the analysis equipment 214 predicted theperformance of candidate strain designs. The analysis equipment 214ranked candidates from “best” to “worst” based on predicted performancewith respect to two phenotypes of interest (yield and productivity).Specifically, the analysis equipment 214 used a weighted sum to score acandidate strain:Score=0.8*yield/max(yields)+0.2*prod/max(prods),where yield represents predicted yield for the candidate strain,max(yields) represents the maximum yield over all candidate strains,prod represents productivity for the candidate strain, andmax(prods) represents the maximum yield over all candidate strains.

The analysis equipment 214 generated a final set of recommendations fromthe ranked list of candidates by imposing both capacity constraints andoperational constraints. In this example, the capacity limit was set at48 computer-generated candidate design strains. Due to operationalconstraints, in this example only one seed strain was used per column ofa 96-well plate. This means that after a seed strain was chosen, up to 8changes to that strain could be built, but only 6 seed strains could bechosen in any given week.

The trained model (described above) was used to predict the expectedperformance (for yield and productivity) of each candidate strain. Theanalysis equipment 214 ranked the candidate strains using the scoringfunction given above. Capacity and operational constraints were appliedto yield a filtered set of 48 candidate strains. This set of filteredcandidate strains is depicted in FIG. 48.

Filtered candidate strains were built (at the factory 210) based on afactory order generated by the order placement engine 208 (3312). Theorder was based upon DNA specifications corresponding to the candidatestrains.

In practice, the build process has an expected failure rate whereby arandom set of strains is not built. For this build cycle, roughly 20% ofthe candidate strains failed build, resulting in 37 built strains.

The analysis equipment 214 was used to measure the actual yield andproductivity performance of the selected strains. The analysis equipment214 evaluated the model and recommended strains based on three criteria:model accuracy; improvement in strain performance; and equivalence (orimprovement) to human expert-generated designs.

The yield and productivity phenotypes were measured for recommendedstrains and compared to the values predicted by the model. As shown inFIG. 49, the model demonstrates useful predictive utility. Inparticular, the predicted yield values for the recommended strains havea Pearson-r correlation coefficient of 0.59 with the correspondingobservations.

Next, the analysis equipment 214 computed percentage performance changefrom the parent strain for each of the recommended strains. This data isshown in FIG. 50 (in light gray). The inventors found that many of thepredicted strains in fact exhibited the expected performance gains withrespect to their immediate parents. In particular, the best predictedstrain showed a 6% improvement in yield with respect to its immediateparent.

In parallel with the model-based strain design process described above,a collection of 48 strains was independently designed by a human expert.Of these strains, 37 were successfully built and tested. This datademonstrated that the model-based strain designs performed comparably tostrains designed by human experts. These experts are highly-skilled(e.g., Ph.D.-level) scientists employed or otherwise engaged by theassignee of the present invention, and familiar with the embodiments ofthis disclosure. To compare the two methods, the inventors firstinspected the performance distributions of each group (FIG. 51). In thisexperiment, the mean yield of model-based strains showed a 1% increasewith respect to human expert generated designs.

The inventors then compared human expert-designed andcomputer-model-designed strains grouped by background, i.e., new strainswith the same parent (FIG. 52). Again, the inventors found thatcomputer-generated designs perform comparably to, and in some casesbetter than, the human expert-generated designs, and further tend toproduce less variability. Finally, the inventors compared the percentagechange with respect to the parent strains of the human expert andmodel-designed strains (FIG. 50). Again, these populations showedcomparable gains.

See Table 4.1 for tabulated summary statistics.

TABLE 4.1 Measured performance statistics for strains designed by thepredictive model and by a human expert reference. Productivity Yieldchange Productivity change from parent Yield [AU] from parent [%] [AU][%] design method computer count 37 37 37 37 model mean 1.0580681080.3578340 0.737928919 −2.5428848 std 0.017811031 1.8293665 0.0836198049.6743873 min 1.015310000 −4.5346677 0.572780000 −23.3626353 median1.058710000 0.005007939 0.766870000 −1.1824159 max 1.093510000 6.00973090.872790000 26.6124119 Human count 37 37 37 37 expert mean 1.038804595−0.0005237 0.748320811 −1.6126436 std 0.032053625 1.9227716 0.1205274689.8530758 min 0.964910000 −3.1043233 0.535980000 −21.4589256 median1.045530000 0.0449168 0.760300000 −1.9241048 max 1.094790000 7.84871740.984110000 21.7335193

At the conclusion of each round of the prediction→build→test cycle, theinventors were interested in evaluating the quality of the modelpredictions and iteratively incorporating new data into the previousmodel. For the former—model evaluation—the inventors focused onmeasuring predictive accuracy by comparing model predictions withexperimental measurements. Predictive accuracy can be assessed throughseveral methods, including a correlation coefficient indicating thestrength of association between the predicted and observed values, orthe root-mean-square error, which is a measure of the average modelerror.

Over many rounds of experimentation, model predictions may drift, andnew genetic changes may be added to the training inputs to improvepredictive accuracy. For this example, design changes and theirresulting performance were added to the predictive model (3316).

Genomic Design and Engineering as a Service

In embodiments of the disclosure, the LIMS system software 3210 of FIG.31 may be implemented in a cloud computing system 3202 of FIG. 32, toenable multiple users to design and build microbial strains according toembodiments of the present disclosure. FIG. 32 illustrates a cloudcomputing environment 3204 according to embodiments of the presentdisclosure. Client computers 3206, such as those illustrated in FIG. 34,access the LIMS system via a network 3208, such as the Internet. Inembodiments, the LIMS system application software 3210 resides in thecloud computing system 3202. The LIMS system may employ one or morecomputing systems using one or more processors, of the type illustratedin FIG. 34. The cloud computing system itself includes a networkinterface 3212 to interface the LIMS system applications 3210 to theclient computers 3206 via the network 3208. The network interface 3212may include an application programming interface (API) to enable clientapplications at the client computers 3206 to access the LIMS systemsoftware 3210. In particular, through the API, client computers 3206 mayaccess components of the LIMS system 200, including without limitationthe software running the input interface 202, the interpreter 204, theexecution engine 207, the order placement engine 208, the factory 210,as well as test equipment 212 and analysis equipment 214. A software asa service (SaaS) software module 3214 offers the LIMS system software3210 as a service to the client computers 3206. A cloud managementmodule 3216 manages access to the LIMS system 3210 by the clientcomputers 3206. The cloud management module 3216 may enable a cloudarchitecture that employs multitenant applications, virtualization orother architectures known in the art to serve multiple users.

Genomic Automation

Automation of the methods of the present disclosure enableshigh-throughput phenotypic screening and identification of targetproducts from multiple test strain variants simultaneously.

The aforementioned genomic engineering predictive modeling platform ispremised upon the fact that hundreds and thousands of mutant strains areconstructed in a high-throughput fashion. The robotic and computersystems described below are the structural mechanisms by which such ahigh-throughput process can be carried out.

In some embodiments, the present disclosure teaches methods of improvinghost cell productivities, or rehabilitating industrial strains. As partof this process, the present disclosure teaches methods of assemblingDNA, building new strains, screening cultures in plates, and screeningcultures in models for tank fermentation. In some embodiments, thepresent disclosure teaches that one or more of the aforementionedmethods of creating and testing new host strains is aided by automatedrobotics.

In some embodiments, the present disclosure teaches a high-throughputstrain engineering platform as depicted in FIG. 6.

HTP Robotic Systems

In some embodiments, the automated methods of the disclosure comprise arobotic system. The systems outlined herein are generally directed tothe use of 96- or 384-well microtiter plates, but as will be appreciatedby those in the art, any number of different plates or configurationsmay be used. In addition, any or all of the steps outlined herein may beautomated; thus, for example, the systems may be completely or partiallyautomated.

In some embodiments, the automated systems of the present disclosurecomprise one or more work modules. For example, in some embodiments, theautomated system of the present disclosure comprises a DNA synthesismodule, a vector cloning module, a strain transformation module, ascreening module, and a sequencing module (see FIG. 7).

As will be appreciated by those in the art, an automated system caninclude a wide variety of components, including, but not limited to:liquid handlers; one or more robotic arms; plate handlers for thepositioning of microplates; plate sealers, plate piercers, automated lidhandlers to remove and replace lids for wells on non-cross contaminationplates; disposable tip assemblies for sample distribution withdisposable tips; washable tip assemblies for sample distribution; 96well loading blocks; integrated thermal cyclers; cooled reagent racks;microtiter plate pipette positions (optionally cooled); stacking towersfor plates and tips; magnetic bead processing stations; filtrationssystems; plate shakers; barcode readers and applicators; and computersystems.

In some embodiments, the robotic systems of the present disclosureinclude automated liquid and particle handling enabling high-throughputpipetting to perform all the steps in the process of gene targeting andrecombination applications. This includes liquid and particlemanipulations such as aspiration, dispensing, mixing, diluting, washing,accurate volumetric transfers; retrieving and discarding of pipettetips; and repetitive pipetting of identical volumes for multipledeliveries from a single sample aspiration. These manipulations arecross-contamination-free liquid, particle, cell, and organism transfers.The instruments perform automated replication of microplate samples tofilters, membranes, and/or daughter plates, high-density transfers,full-plate serial dilutions, and high capacity operation.

In some embodiments, the customized automated liquid handling system ofthe disclosure is a TECAN machine (e.g. a customized TECAN Freedom Evo).

In some embodiments, the automated systems of the present disclosure arecompatible with platforms for multi-well plates, deep-well plates,square well plates, reagent troughs, test tubes, mini tubes, microfugetubes, cryovials, filters, micro array chips, optic fibers, beads,agarose and acrylamide gels, and other solid-phase matrices or platformsare accommodated on an upgradeable modular deck. In some embodiments,the automated systems of the present disclosure contain at least onemodular deck for multi-position work surfaces for placing source andoutput samples, reagents, sample and reagent dilution, assay plates,sample and reagent reservoirs, pipette tips, and an active tip-washingstation.

In some embodiments, the automated systems of the present disclosureinclude high-throughput electroporation systems. In some embodiments,the high-throughput electroporation systems are capable of transformingcells in 96 or 384-well plates. In some embodiments, the high-throughputelectroporation systems include VWR® High-throughput ElectroporationSystems, BTX™, Bio-Rad® Gene Pulser MXcell™ or other multi-wellelectroporation system.

In some embodiments, the integrated thermal cycler and/or thermalregulators are used for stabilizing the temperature of heat exchangerssuch as controlled blocks or platforms to provide accurate temperaturecontrol of incubating samples from 0° C. to 100° C.

In some embodiments, the automated systems of the present disclosure arecompatible with interchangeable machine-heads (single or multi-channel)with single or multiple magnetic probes, affinity probes, replicators orpipetters, capable of robotically manipulating liquid, particles, cells,and multi-cellular organisms. Multi-well or multi-tube magneticseparators and filtration stations manipulate liquid, particles, cells,and organisms in single or multiple sample formats.

In some embodiments, the automated systems of the present disclosure arecompatible with camera vision and/or spectrometer systems. Thus, in someembodiments, the automated systems of the present disclosure are capableof detecting and logging color and absorption changes in ongoingcellular cultures.

In some embodiments, the automated system of the present disclosure isdesigned to be flexible and adaptable with multiple hardware add-ons toallow the system to carry out multiple applications. The softwareprogram modules allow creation, modification, and running of methods.The system's diagnostic modules allow setup, instrument alignment, andmotor operations. The customized tools, labware, and liquid and particletransfer patterns allow different applications to be programmed andperformed. The database allows method and parameter storage. Robotic andcomputer interfaces allow communication between instruments.

Thus, in some embodiments, the present disclosure teaches ahigh-throughput strain engineering platform, as depicted in FIG. 26.

Persons having skill in the art will recognize the various roboticplatforms capable of carrying out the HTP engineering methods of thepresent disclosure. Table 5 below provides a non-exclusive list ofscientific equipment capable of carrying out each step of the HTPengineering steps of the present disclosure as described in FIG. 26.

TABLE 5 Non-exclusive list of Scientific Equipment Compatible with theHTP engineering methods of the present disclosure. EquipmentOperation(s) Compatible Equipment Type performedMake/Model/Configuration Acquire and liquid handlers Hitpicking(combining by Hamilton Microlab STAR, build DNA pieces transferring)Labcyte Echo 550, Tecan EVO primers/templates for PCR 200, BeckmanCoulter Biomek amplification of DNA parts FX, or equivalents Thermalcyclers PCR amplification of DNA Inheco Cycler, ABI 2720, ABI partsProflex 384, ABI Veriti, or equivalents QC DNA parts Fragment gelelectrophoresis to Agilent Bioanalyzer, AATI analyzers confirm PCRproducts of Fragment Analyzer, or (capillary appropriate sizeequivalents electrophoresis) Sequencer Verifying sequence of BeckmanCeq-8000, Beckman (sanger: parts/templates GenomeLab ™, or equivalentsBeckman) NGS (next Verifying sequence of Illumina Mi Seq seriesgeneration parts/templates sequences, illumina Hi-Seq, Ion sequencing)torrent, pac bio or other instrument equivalents nanodrop/plateassessing concentration of Molecular Devices SpectraMax reader DNAsamples M5, Tecan M1000, or equivalents. Generate DNA liquid handlersHitpicking (combining by Hamilton Microlab STAR, assembly transferring)DNA parts for Labcyte Echo 550, Tecan EVO assembly along with 200,Beckman Coulter Biomek cloning vector, addition of FX, or equivalentsreagents for assembly reaction/process QC DNA assembly Colony pickersfor inoculating colonies in Scirobotics Pickolo, Molecular liquid mediaDevices QPix 420 liquid handlers Hitpicking Hamilton Microlab STAR,primers/templates, diluting Labcyte Echo 550, Tecan EVO samples 200,Beckman Coulter Biomek FX, or equivalents Fragment gel electrophoresisto Agilent Bioanalyzer, AATI analyzers confirm assembled FragmentAnalyzer (capillary products of appropriate electrophoresis) sizeSequencer Verifying sequence of ABI3730 Thermo Fisher, (sanger:assembled plasmids Beckman Ceq-8000, Beckman Beckman) GenomeLab ™, orequivalents NGS (next Verifying sequence of Illumina MiSeq seriesgeneration assembled plasmids sequences, illumina Hi-Seq, Ionsequencing) torrent, pac bio or other instrument equivalents Preparebase strain and DNA centrifuge spinning/pelleting cells Beckman Avantifloor assembly centrifuge, Hettich Centrifuge Transform DNA into baseElectroporators electroporative BTX Gemini X2, BIO-RAD straintransformation of cells MicroPulser Electroporator Ballistic ballistictransformation of BIO-RAD PDS1000 transformation cells Incubators, forchemical Inheco Cycler, ABI 2720, ABI thermal cyclerstransformation/heat shock Proflex 384, ABI Veriti, or equivalents Liquidhandlers for combining DNA, cells, Hamilton Microlab STAR, bufferLabcyte Echo 550, Tecan EVO 200, Beckman Coulter Biomek FX, orequivalents Integrate DNA into Colony pickers for inoculating coloniesin Scirobotics Pickolo, Molecular genome of base strain liquid mediaDevices QPix 420 Liquid handlers For transferring cells onto HamiltonMicrolab STAR, Agar, transferring from Labcyte Echo 550, Tecan EVOculture plates to different 200, Beckman Coulter Biomek culture plates(inoculation FX, or equivalents into other selective media) Platformshaker- incubation with shaking of Kuhner Shaker ISF4-X, Infors-htincubators microtiter plate cultures Multitron Pro QC transformed strainColony pickers for inoculating colonies in Scirobotics Pickolo,Molecular liquid media Devices QPix 420 liquid handlers HitpickingHamilton Microlab STAR, primers/templates, diluting Labcyte Echo 550,Tecan EVO samples 200, Beckman Coulter Biomek FX, or equivalents Thermalcyclers cPCR verification of Inheco Cycler, ABI 2720, ABI strainsProflex 384, ABI Veriti, or equivalents Fragment gel electrophoresis toInfors-ht Multitron Pro, Kuhner analyzers confirm cPCR products ofShaker ISF4-X (capillary appropriate size electrophoresis) SequencerSequence verification of Beckman Ceq-8000, Beckman (sanger: introducedmodification GenomeLab ™, or equivalents Beckman) NGS (next Sequenceverification of Illumina Mi Seq series generation introducedmodification sequences, illumina Hi-Seq, Ion sequencing) torrent, pacbio or other instrument equivalents Select and consolidate Liquidhandlers For transferring from Hamilton Microlab STAR, QC'd strains intoculture plates to different Labcyte Echo 550, Tecan EVO test plateculture plates (inoculation 200, Beckman Coulter Biomek into productionmedia) FX, or equivalents Colony pickers for inoculating colonies inScirobotics Pickolo, Molecular liquid media Devices QPix 420 Platformshaker- incubation with shaking of Kuhner Shaker ISF4-X, Infors-htincubators microtiter plate cultures Multitron Pro Culture strains inLiquid handlers For transferring from Hamilton Microlab STAR, seedplates culture plates to different Labcyte Echo 550, Tecan EVO cultureplates (inoculation 200, Beckman Coulter Biomek into production media)FX, or equivalents Platform shaker- incubation with shaking of KuhnerShaker ISF4-X, Infors-ht incubators microtiter plate cultures MultitronPro liquid dispensers Dispense liquid culture Well mate (Thermo), mediainto microtiter plates Benchcel2R (velocity 11), plateloc (velocity 11)microplate apply barcoders to plates Microplate labeler (a2+ cab-labeler agilent), benchcell 6R (velocity 11) Generate product Liquidhandlers For transferring from Hamilton Microlab STAR, from strainculture plates to different Labcyte Echo 550, Tecan EVO culture plates(inoculation 200, Beckman Coulter Biomek into production media) FX, orequivalents Platform shaker- incubation with shaking of Kuhner ShakerISF4-X, Infors-ht incubators microtiter plate cultures Multitron Proliquid dispensers Dispense liquid culture well mate (Thermo), media intomultiple Benchcel2R (velocity 11), microtiter plates and seal plateloc(velocity 11) plates microplate Apply barcodes to plates microplatelabeler (a2+ cab- labeler agilent), benchcell 6R (velocity 11) EvaluateLiquid handlers For processing culture Hamilton Microlab STAR,performance broth for downstream Labcyte Echo 550, Tecan EVO analytical200, Beckman Coulter Biomek FX, or equivalents UHPLC, HPLC quantitativeanalysis of Agilent 1290 Series UHPLC precursor and target and 1200Series HPLC with UV compounds and RI detectors, or equivalent; also anyLC/MS LC/MS highly specific analysis of Agilent 6490 QQQ and 6550precursor and target QTOF coupled to 1290 Series compounds as well asside UHPLC and degradation products Spectrophotometer Quantification ofdifferent Tecan M1000, spectramax M5, compounds using Genesys 10Sspectrophotometer based assays Culture strains in Fermenters: incubationwith shaking Sartorius, DASGIPs flasks (Eppendorf), BIO-FLOs(Sartorius-stedim). Applikon Platform shakers innova 4900, or anyequivalent Generate product Fermenters: DASGIPs (Eppendorf), BIO-FLOs(Sartorius-stedim) from strain Evaluate Liquid handlers For transferringfrom Hamilton Microlab STAR, performance culture plates to differentLabcyte Echo 550, Tecan EVO culture plates (inoculation 200, BeckmanCoulter Biomek into production media) FX, or equivalents UHPLC, HPLCquantitative analysis of Agilent 1290 Series UHPLC precursor and targetand 1200 Series HPLC with UV compounds and RI detectors, or equivalent;also any LC/MS LC/MS highly specific analysis of Agilent 6490 QQQ and6550 precursor and target QTOF coupled to 1290 Series compounds as wellas side UHPLC and degradation products Flow cytometer Characterizestrain BD Accuri, Millipore Guava performance (measure viability)Spectrophotometer Characterize strain Tecan M1000, Spectramax M5,performance (measure or other equivalents biomass)

Computer System Hardware

FIG. 34 illustrates an example of a computer system 800 that may be usedto execute program code stored in a non-transitory computer readablemedium (e.g., memory) in accordance with embodiments of the disclosure.The computer system includes an input/output subsystem 802, which may beused to interface with human users and/or other computer systemsdepending upon the application. The I/O subsystem 802 may include, e.g.,a keyboard, mouse, graphical user interface, touchscreen, or otherinterfaces for input, and, e.g., an LED or other flat screen display, orother interfaces for output, including application program interfaces(APIs). Other elements of embodiments of the disclosure, such as thecomponents of the LIMS system, may be implemented with a computer systemlike that of computer system 800.

Program code may be stored in non-transitory media such as persistentstorage in secondary memory 810 or main memory 808 or both. Main memory808 may include volatile memory such as random access memory (RAM) ornon-volatile memory such as read only memory (ROM), as well as differentlevels of cache memory for faster access to instructions and data.Secondary memory may include persistent storage such as solid statedrives, hard disk drives or optical disks. One or more processors 804reads program code from one or more non-transitory media and executesthe code to enable the computer system to accomplish the methodsperformed by the embodiments herein. Those skilled in the art willunderstand that the processor(s) may ingest source code, and interpretor compile the source code into machine code that is understandable atthe hardware gate level of the processor(s) 804. The processor(s) 804may include graphics processing units (GPUs) for handlingcomputationally intensive tasks. Particularly in machine learning, oneor more CPUs 804 may offload the processing of large quantities of datato one or more GPUs 804.

The processor(s) 804 may communicate with external networks via one ormore communications interfaces 807, such as a network interface card,WiFi transceiver, etc. A bus 805 communicatively couples the I/Osubsystem 802, the processor(s) 804, peripheral devices 806,communications interfaces 807, memory 808, and persistent storage 810.Embodiments of the disclosure are not limited to this representativearchitecture. Alternative embodiments may employ different arrangementsand types of components, e.g., separate buses for input-outputcomponents and memory subsystems.

Those skilled in the art will understand that some or all of theelements of embodiments of the disclosure, and their accompanyingoperations, may be implemented wholly or partially by one or morecomputer systems including one or more processors and one or more memorysystems like those of computer system 800. In particular, the elementsof the LIMS system 200 and any robotics and other automated systems ordevices described herein may be computer-implemented. Some elements andfunctionality may be implemented locally and others may be implementedin a distributed fashion over a network through different servers, e.g.,in client-server fashion, for example. In particular, server-sideoperations may be made available to multiple clients in a software as aservice (SaaS) fashion, as shown in FIG. 32.

The term component in this context refers broadly to software, hardware,or firmware (or any combination thereof) component. Components aretypically functional components that can generate useful data or otheroutput using specified input(s). A component may or may not beself-contained. An application program (also called an “application”)may include one or more components, or a component can include one ormore application programs.

Some embodiments include some, all, or none of the components along withother modules or application components. Still yet, various embodimentsmay incorporate two or more of these components into a single moduleand/or associate a portion of the functionality of one or more of thesecomponents with a different component.

The term “memory” can be any device or mechanism used for storinginformation. In accordance with some embodiments of the presentdisclosure, memory is intended to encompass any type of, but is notlimited to: volatile memory, nonvolatile memory, and dynamic memory. Forexample, memory can be random access memory, memory storage devices,optical memory devices, magnetic media, floppy disks, magnetic tapes,hard drives, SIMMs, SDRAM, DIMMs, RDRAM, DDR RAM, SODIMMS, erasableprogrammable read-only memories (EPROMs), electrically erasableprogrammable read-only memories (EEPROMs), compact disks, DVDs, and/orthe like. In accordance with some embodiments, memory may include one ormore disk drives, flash drives, databases, local cache memories,processor cache memories, relational databases, flat databases, servers,cloud based platforms, and/or the like. In addition, those of ordinaryskill in the art will appreciate many additional devices and techniquesfor storing information can be used as memory.

Memory may be used to store instructions for running one or moreapplications or modules on a processor. For example, memory could beused in some embodiments to house all or some of the instructions neededto execute the functionality of one or more of the modules and/orapplications disclosed in this application.

HTP Microbial Strain Engineering Based Upon Genetic Design Predictions:An Example Workflow

In some embodiments, the present disclosure teaches the directedengineering of new host organisms based on the recommendations of thecomputational analysis systems of the present disclosure.

In some embodiments, the present disclosure is compatible with allgenetic design and cloning methods. That is, in some embodiments, thepresent disclosure teaches the use of traditional cloning techniquessuch as polymerase chain reaction, restriction enzyme digestions,ligation, homologous recombination, RT PCR, and others generally knownin the art and are disclosed in for example: Sambrook et al. (2001)Molecular Cloning: A Laboratory Manual (3rd ed., Cold Spring HarborLaboratory Press, Plainview, N.Y.), incorporated herein by reference.

In some embodiments, the cloned sequences can include possibilities fromany of the HTP genetic design libraries taught herein, for example:promoters from a promoter swap library, SNPs from a SNP swap library,start or stop codons from a start/stop codon exchange library,terminators from a STOP swap library, or sequence optimizations from asequence optimization library.

Further, the exact sequence combinations that should be included in aparticular construct can be informed by the epistatic mapping function.

In other embodiments, the cloned sequences can also include sequencesbased on rational design (hypothesis-driven) and/or sequences based onother sources, such as scientific publications.

In some embodiments, the present disclosure teaches methods of directedengineering, including the steps of i) generating custom-madeSNP-specific DNA, ii) assembling SNP-specific plasmids, iii)transforming target host cells with SNP-specific DNA, and iv) loopingout any selection markers (See FIG. 2).

FIG. 6A depicts the general workflow of the strain engineering methodsof the present disclosure, including acquiring and assembling DNA,assembling vectors, transforming host cells and removing selectionmarkers.

Build Specific DNA Oligonucleotides

In some embodiments, the present disclosure teaches inserting and/orreplacing and/or altering and/or deleting a DNA segment of the host cellorganism. In some aspects, the methods taught herein involve building anoligonucleotide of interest (i.e. a target DNA segment), that will beincorporated into the genome of a host organism. In some embodiments,the target DNA segments of the present disclosure can be obtained viaany method known in the art, including: copying or cutting from a knowntemplate, mutation, or DNA synthesis. In some embodiments, the presentdisclosure is compatible with commercially available gene synthesisproducts for producing target DNA sequences (e.g., GeneArt™, GeneMaker™,GenScript™, Anagen™, Blue Heron™, Entelechon™, GeNOsys, Inc., orQiagen™).

In some embodiments, the target DNA segment is designed to incorporate aSNP into a selected DNA region of the host organism (e.g., adding abeneficial SNP). In other embodiments, the DNA segment is designed toremove a SNP from the DNA of the host organisms (e.g., removing adetrimental or neutral SNP).

In some embodiments, the oligonucleotides used in the inventive methodscan be synthesized using any of the methods of enzymatic or chemicalsynthesis known in the art. The oligonucleotides may be synthesized onsolid supports such as controlled pore glass (CPG), polystyrene beads,or membranes composed of thermoplastic polymers that may contain CPG.Oligonucleotides can also be synthesized on arrays, on a parallelmicroscale using microfluidics (Tian et al., Mol. BioSyst., 5, 714-722(2009)), or known technologies that offer combinations of both (seeJacobsen et al., U.S. Pat. App. No. 2011/0172127).

Synthesis on arrays or through microfluidics offers an advantage overconventional solid support synthesis by reducing costs through lowerreagent use. The scale required for gene synthesis is low, so the scaleof oligonucleotide product synthesized from arrays or throughmicrofluidics is acceptable. However, the synthesized oligonucleotidesare of lesser quality than when using solid support synthesis (See Tianinfra.; see also Staehler et al., U.S. Pat. App. No. 2010/0216648).

A great number of advances have been achieved in the traditionalfour-step phosphoramidite chemistry since it was first described in the1980s (see for example, Sierzchala, et al. J. Am. Chem. Soc., 125,13427-13441 (2003) using peroxy anion deprotection; Hayakawa et al.,U.S. Pat. No. 6,040,439 for alternative protecting groups; Azhayev etal, Tetrahedron 57, 4977-4986 (2001) for universal supports; Kozlov etal., Nucleosides, Nucleotides, and Nucleic Acids, 24 (5-7), 1037-1041(2005) for improved synthesis of longer oligonucleotides through the useof large-pore CPG; and Damha et al., NAR, 18, 3813-3821 (1990) forimproved derivatization).

Regardless of the type of synthesis, the resulting oligonucleotides maythen form the smaller building blocks for longer oligonucleotides. Insome embodiments, smaller oligonucleotides can be joined together usingprotocols known in the art, such as polymerase chain assembly (PCA),ligase chain reaction (LCR), and thermodynamically balanced inside-outsynthesis (TBIO) (see Czar et al. Trends in Biotechnology, 27, 63-71(2009)). In PCA, oligonucleotides spanning the entire length of thedesired longer product are annealed and extended in multiple cycles(typically about 55 cycles) to eventually achieve full-length product.LCR uses ligase enzyme to join two oligonucleotides that are bothannealed to a third oligonucleotide. TBIO synthesis starts at the centerof the desired product and is progressively extended in both directionsby using overlapping oligonucleotides that are homologous to the forwardstrand at the 5′ end of the gene and against the reverse strand at the3′ end of the gene.

Another method of synthesizing a larger double stranded DNA fragment isto combine smaller oligonucleotides through top-strand PCR (TSP). Inthis method, a plurality of oligonucleotides spans the entire length ofa desired product and contain overlapping regions to the adjacentoligonucleotide(s). Amplification can be performed with universalforward and reverse primers, and through multiple cycles ofamplification a full-length double stranded DNA product is formed. Thisproduct can then undergo optional error correction and furtheramplification that results in the desired double stranded DNA fragmentend product.

In one method of TSP, the set of smaller oligonucleotides that will becombined to form the full-length desired product are between 40-200bases long and overlap each other by at least about 15-20 bases. Forpractical purposes, the overlap region should be at a minimum longenough to ensure specific annealing of oligonucleotides and have a highenough melting temperature (Tm) to anneal at the reaction temperatureemployed. The overlap can extend to the point where a givenoligonucleotide is completely overlapped by adjacent oligonucleotides.The amount of overlap does not seem to have any effect on the quality ofthe final product. The first and last oligonucleotide building block inthe assembly should contain binding sites for forward and reverseamplification primers. In one embodiment, the terminal end sequence ofthe first and last oligonucleotide contain the same sequence ofcomplementarity to allow for the use of universal primers.

Assembling/Cloning Custom Plasmids

In some embodiments, the present disclosure teaches methods forconstructing vectors capable of inserting desired target DNA sections(e.g. containing a particular SNP) into the genome of host organisms. Insome embodiments, the present disclosure teaches methods of cloningvectors comprising the target DNA, homology arms, and at least oneselection marker (see FIG. 3).

In some embodiments, the present disclosure is compatible with anyvector suited for transformation into the host organism. In someembodiments, the present disclosure teaches use of shuttle vectorscompatible with a host cell. In one embodiment, a shuttle vector for usein the methods provided herein is a shuttle vector compatible with an E.coli and/or Corynebacterium host cell. Shuttle vectors for use in themethods provided herein can comprise markers for selection and/orcounter-selection as described herein. The markers can be any markersknown in the art and/or provided herein. The shuttle vectors can furthercomprise any regulatory sequence(s) and/or sequences useful in theassembly of said shuttle vectors as known in the art. The shuttlevectors can further comprise any origins of replication that may beneeded for propagation in a host cell as provided herein such as, forexample, E. coli or C. glutamicum. The regulatory sequence can be anyregulatory sequence known in the art or provided herein such as, forexample, a promoter, start, stop, signal, secretion and/or terminationsequence used by the genetic machinery of the host cell. In certaininstances, the target DNA can be inserted into vectors, constructs orplasmids obtainable from any repository or catalogue product, such as acommercial vector (see e.g., DNA2.0 custom or GATEWAY® vectors). Incertain instances, the target DNA can be inserted into vectors,constructs or plasmids obtainable from any repository or catalogueproduct, such as a commercial vector (see e.g., DNA2.0 custom orGATEWAY® vectors).

In some embodiments, the assembly/cloning methods of the presentdisclosure may employ at least one of the following assemblystrategies: 1) type II conventional cloning, ii) type II S-mediated or“Golden Gate” cloning (see, e.g., Engler, C., R. Kandzia, and S.Marillonnet. 2008 “A one pot, one step, precision cloning method withhigh-throughput capability”. PLos One 3:e3647; Kotera, I., and T. Nagai.2008 “A high-throughput and single-tube recombination of crude PCRproducts using a DNA polymerase inhibitor and type IIS restrictionenzyme.” J Biotechnol 137:1-7.; Weber, E., R. Gruetzner, S. Werner, C.Engler, and S. Marillonnet. 2011 Assembly of Designer TAL Effectors byGolden Gate Cloning. PloS One 6:e19722), iii) GATEWAY® recombination,iv) TOPO® cloning, exonuclease-mediated assembly (Aslanidis and de Jong1990. “Ligation-independent cloning of PCR products (LIC-PCR).” NucleicAcids Research, Vol. 18, No. 20 6069), v) homologous recombination, vi)non-homologous end joining, vii) Gibson assembly (Gibson et al., 2009“Enzymatic assembly of DNA molecules up to several hundred kilobases”Nature Methods 6, 343-345) or a combination thereof. Modular type IISbased assembly strategies are disclosed in PCT Publication WO2011/154147, the disclosure of which is incorporated herein byreference.

In some embodiments, the present disclosure teaches cloning vectors withat least one selection marker. Various selection marker genes are knownin the art often encoding antibiotic resistance function for selectionin prokaryotic (e.g., against ampicillin, kanamycin, tetracycline,chloramphenicol, zeocin, spectinomycin/streptomycin) or eukaryotic cells(e.g. geneticin, neomycin, hygromycin, puromycin, blasticidin, zeocin)under selective pressure. Other marker systems allow for screening andidentification of wanted or unwanted cells such as the well-knownblue/white screening system used in bacteria to select positive clonesin the presence of X-gal or fluorescent reporters such as green or redfluorescent proteins expressed in successfully transduced host cells.Another class of selection markers most of which are only functional inprokaryotic systems relates to counter selectable marker genes oftenalso referred to as “death genes” which express toxic gene products thatkill producer cells. Examples of such genes include sacB, rpsL(strA),tetAR, pheS, thyA, gata-1, or ccdB, the function of which is describedin (Reyrat et al. 1998 “Counterselectable Markers: Untapped Tools forBacterial Genetics and Pathogenesis.” Infect Immun. 66(9): 4011-4017).

Protoplasting Methods

In one embodiment, the methods and systems provided herein make use ofthe generation of protoplasts from filamentous fungal cells. Suitableprocedures for preparation of protoplasts can be any known in the artincluding, for example, those described in EP 238,023 and Yelton et al.(1984, Proc. Natl. Acad. Sci. USA 81:1470-1474). In one embodiment,protoplasts are generated by treating a culture of filamentous fungalcells with one or more lytic enzymes or a mixture thereof. The lyticenzymes can be a beta-glucanase and/or a polygalacturonase. In oneembodiment, the enzyme mixture for generating protoplasts is VinoTasteconcentrate. Following enzymatic treatment, the protoplasts can beisolated using methods known in the art such as, for example,centrifugation.

The pre-cultivation and the actual protoplasting step can be varied tooptimize the number of protoplasts and the transformation efficiency.For example, there can be variations of inoculum size, inoculum method,pre-cultivation media, pre-cultivation times, pre-cultivationtemperatures, mixing conditions, washing buffer composition, dilutionratios, buffer composition during lytic enzyme treatment, the typeand/or concentration of lytic enzyme used, the time of incubation withlytic enzyme, the protoplast washing procedures and/or buffers, theconcentration of protoplasts and/or polynucleotide and/or transformationreagents during the actual transformation, the physical parametersduring the transformation, the procedures following the transformationup to the obtained transformants.

Protoplasts can be resuspended in an osmotic stabilizing buffer. Thecomposition of such buffers can vary depending on the species,application and needs. However, typically these buffers contain eitheran organic component like sucrose, citrate, mannitol or sorbitol between0.5 and 2 M. More preferably between 0.75 and 1.5 M; most preferred is 1M. Otherwise these buffers contain an inorganic osmotic stabilizingcomponent like KCl, MgSO.sub.4, NaCl or MgCl.sub.2 in concentrationsbetween 0.1 and 1.5 M. Preferably between 0.2 and 0.8 M; more preferablybetween 0.3 and 0.6 M, most preferably 0.4 M. The most preferredstabilizing buffers are STC (sorbitol, 0.8 M; CaCl.sub.2, 25 mM; Tris,25 mM; pH 8.0) or KC1-citrate (KCl, 0.3-0.6 M; citrate, 0.2% (w/v)). Theprotoplasts can be used in a concentration between 1×10⁵ and 1×10¹⁰cells/ml. Preferably, the concentration is between 1×10⁶ and 1×10⁹; morepreferably the concentration is between 1×10⁷ and 5×10⁸; most preferablythe concentration is 1×10⁸ cells/ml. DNA is used in a concentrationbetween 0.01 and 10 ug; preferably between 0.1 and 5 ug, even morepreferably between 0.25 and 2 ug; most preferably between 0.5 and 1 ug.To increase the efficiency of transfection carrier DNA (as salmon spermDNA or non-coding vector DNA) may be added to the transformationmixture.

In one embodiment, following generation and subsequent isolation, theprotoplasts are mixed with one or more cryoprotectants. Thecryoprotectants can be glycols, dimethyl sulfoxide (DMSO), polyols,sugars, 2-Methyl-2,4-pentanediol (MPD), polyvinylpyrrolidone (PVP),methylcellulose, C-linked antifreeze glycoproteins (C-AFGP) orcombinations thereof. Glycols for use as cryoprotectants in the methodsand systems provided herein can be selected from ethylene glycol,propylene glycol, polypropylene glycol (PEG), glycerol, or combinationsthereof. Polyols for use as cryoprotectants in the methods and systemsprovided herein can be selected from propane-1,2-diol, propane-1,3-diol,1,1,1-tris-(hydroxymethyl)ethane (THME), and2-ethyl-2-(hydroxymethyl)-propane-1,3-diol (EHMP), or combinationsthereof. Sugars for use as cryoprotectants in the methods and systemsprovided herein can be selected from trehalose, sucrose, glucose,raffinose, dextrose or combinations thereof. In one embodiment, theprotoplasts are mixed with DMSO. DMSO can be mixed with the protoplastsat a final concentration of at least, at most, less than, greater than,equal to, or about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 12.5%, 15%,20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75% w/v orv/v. The protoplasts/cryoprotectant (e.g., DMSO) mixture can bedistributed to microtiter plates prior to storage. Theprotoplast/cryoprotectant (e.g., DMSO) mixture can be stored at anytemperature provided herein for long-term storage (e.g., several hours,day(s), week(s), month(s), year(s)) as provided herein such as, forexample −20° C. or −80° C. In one embodiment, an additionalcryoprotectant (e.g., PEG) is added to the protoplasts/DMSO mixture. Inyet another embodiment, the additional cryoprotectant (e.g., PEG) isadded to the protoplast/DMSO mixture prior to storage. The PEG can beany PEG provided herein and can be added at any concentration (e.g., w/vor v/v) as provided herein.

Protoplast Transformation Methods

In one embodiment, the methods and systems provided herein require thetransfer of nucleic acids to protoplasts derived from filamentous fungalcells as described herein. In another embodiment, the transformationutilized by the methods and systems provided herein is high-throughputin nature and/or is partially or fully automated as described herein.Further to this embodiment, the transformation is performed by addingconstructs or expression constructs as described herein to the wells ofa microtiter plate followed by aliquoting protoplasts generated by themethods provided herein to each well of the microtiter plate. Suitableprocedures for transformation/transfection of protoplasts can be anyknown in the art including, for example, those described ininternational patent applications PCT/NL99/00618, PCT/EP99/202516,Finkelstein and Ball (eds.), Biotechnology of filamentous fungi,technology and products, Butterworth-Heinemann (1992), Bennett andLasure (eds.) More Gene Manipulations in fungi, Academic Press (1991),Turner, in: Puhler (ed), Biotechnology, second completely revisededition, VHC (1992) protoplast fusion, and the Ca-PEG mediatedprotoplast transformation as described in EP635574B. Alternatively,transformation of the filamentous fungal host cells or protoplastsderived therefrom can also be performed by electroporation such as, forexample, the electroporation described by Chakraborty and Kapoor,Nucleic Acids Res. 18:6737 (1990), Agrobacterium tumefaciens-mediatedtransformation, biolistic introduction of DNA such as, for example, asdescribed in Christiansen et al., Curr. Genet. 29:100 102 (1995); Durandet al., Curr. Genet. 31:158 161 (1997); and Barcellos et al., Can. J.Microbiol. 44:1137 1141 (1998) or “magneto-biolistic” transfection ofcells such as, for example, described in U.S. Pat. Nos. 5,516,670 and5,753,477. In one embodiment, the transformation procedure used in themethods and systems provided herein is one amendable to beinghigh-throughput and/or automated as provided herein such as, forexample, PEG mediated transformation.

Transformation of the protoplasts generated using the methods describedherein can be facilitated through the use of any transformation reagentknown in the art. Suitable transformation reagents can be selected fromPolyethylene Glycol (PEG), FUGENE® HD (from Roche), Lipofectamine® orOLIGOFECTAMINE® (from Invitrogen), TRANSPASS®D1 (from New EnglandBiolabs), LYPOVEC® or LIPOGEN® (from Invivogen). In one embodiment, PEGis the most preferred transformation/transfection reagent. PEG isavailable at different molecular weights and can be used at differentconcentrations. Preferably PEG 4000 is used between 10% and 60%, morepreferably between 20% and 50%, most preferably at 30%. In oneembodiment, the PEG is added to the protoplasts prior to storage asdescribed herein.

Transformation of Host Cells

In some embodiments, the vectors of the present disclosure may beintroduced into the host cells using any of a variety of techniques,including transformation, transfection, transduction, viral infection,gene guns, or Ti-mediated gene transfer (see Christie, P. J., andGordon, J. E., 2014 “The Agrobacterium Ti Plasmids” Microbiol SPectr.2014; 2(6); 10.1128). Particular methods include calcium phosphatetransfection, DEAE-Dextran mediated transfection, lipofection, orelectroporation (Davis, L., Dibner, M., Battey, I., 1986 “Basic Methodsin Molecular Biology”). Other methods of transformation include forexample, lithium acetate transformation and electroporation See, e.g.,Gietz et al., Nucleic Acids Res. 27:69-74 (1992); Ito et al., J.Bacterol. 153:163-168 (1983); and Becker and Guarente, Methods inEnzymology 194:182-187 (1991). In some embodiments, transformed hostcells are referred to as recombinant host strains.

In some embodiments, the present disclosure teaches high-throughputtransformation of cells using the 96-well plate robotics platform andliquid handling machines of the present disclosure.

In some embodiments, the present disclosure teaches screeningtransformed cells with one or more selection markers as described above.In one such embodiment, cells transformed with a vector comprising akanamycin resistance marker (KanR) are plated on media containingeffective amounts of the kanamycin antibiotic. Colony forming unitsvisible on kanamycin-laced media are presumed to have incorporated thevector cassette into their genome. Insertion of the desired sequencescan be confirmed via PCR, restriction enzyme analysis, and/or sequencingof the relevant insertion site.

Looping Out of Selected Sequences

In some embodiments, the present disclosure teaches methods of loopingout selected regions of DNA from the host organisms. The looping outmethod can be as described in Nakashima et al. 2014 “Bacterial CellularEngineering by Genome Editing and Gene Silencing.” Int. J. Mol. Sci.15(2), 2773-2793. In some embodiments, the present disclosure teacheslooping out selection markers from positive transformants. Looping outdeletion techniques are known in the art, and are described in (Tear etal. 2014 “Excision of Unstable Artificial Gene-Specific inverted RepeatsMediates Scar-Free Gene Deletions in Escherichia coli.” Appl. Biochem.Biotech. 175:1858-1867). The looping out methods used in the methodsprovided herein can be performed using single-crossover homologousrecombination or double-crossover homologous recombination. In oneembodiment, looping out of selected regions as described herein canentail using single-crossover homologous recombination as describedherein.

First, loop out vectors are inserted into selected target regions withinthe genome of the host organism (e.g., via homologous recombination,CRISPR, or other gene editing technique). In one embodiment,single-crossover homologous recombination is used between a circularplasmid or vector and the host cell genome in order to loop-in thecircular plasmid or vector such as depicted in FIG. 3. The insertedvector can be designed with a sequence which is a direct repeat of anexisting or introduced nearby host sequence, such that the directrepeats flank the region of DNA slated for looping and deletion. Onceinserted, cells containing the loop out plasmid or vector can be counterselected for deletion of the selection region (e.g., see FIG. 4; lack ofresistance to the selection gene).

Persons having skill in the art will recognize that the description ofthe loopout procedure represents but one illustrative method fordeleting unwanted regions from a genome. Indeed the methods of thepresent disclosure are compatible with any method for genome deletions,including but not limited to gene editing via CRISPR, TALENS, FOK, orother endonucleases. Persons skilled in the art will also recognize theability to replace unwanted regions of the genome via homologousrecombination techniques

EXAMPLES

The following examples are given for the purpose of illustrating variousembodiments of the disclosure and are not meant to limit the presentdisclosure in any fashion. Changes therein and other uses which areencompassed within the spirit of the disclosure, as defined by the scopeof the claims, will be recognized by those skilled in the art.

A brief table of contents is provided below solely for the purpose ofassisting the reader. Nothing in this table of contents is meant tolimit the scope of the examples or disclosure of the application.

TABLE 5.1 Table of Contents For Example Section. Example # Title BriefDescription 1 HTP Transformation of Corynebacterium & Describesembodiments of the high Demonstration of SNP Library Creation throughputgenetic engineering methods of the present disclosure. 2 HTP GenomicEngineering-Implementation Describes approaches for of a SNP Library toRehabilitate/Improve an rehabilitating industrial organisms IndustrialMicrobial Strain through SNP swap methods of the present disclosure. 3HTP Genomic Engineering-Implementation Describes an implementation of ofa SNP Swap Library to Improve Strain SNP swap techniques for Performancein Lysine Production in improving the performance of Corynebacterium.Corynebacterium strain producing lysine. Also discloses selected secondand third order mutation consolidations. 4 HTP GenomicEngineering-Implementation Describes methods for improving of a PromoterSwap Library to Improve an the strain performance of host IndustrialMicrobial Strain organisms through PRO swap genetic design libraries ofthe present disclosure. 5 HTP Genomic Engineering-ImplementationDescribes an implementation of of a PRO Swap Library to Improve StrainPRO swap techniques for Performance for Lysine Production improving theperformance of Corynebacterium strain producing lysine. 6 EpistasisMapping-An Algorithmic Tool for Describes an embodiment of thePredicting Beneficial Mutation automated tools/algorithms of theConsolidations present disclosure for predicting beneficial genemutation consolidations. 7 HTP Genomic Engineering-PRO Swap Describesand illustrates the ability Mutation Consolidation and Multi-Factor ofthe HTP methods of the present Combinatorial Testing disclosure toeffectively explore the large solution space created by thecombinatorial consolidation of multiple gene/genetic design librarycombinations. 8 HTP Genomic Engineering-Implementation Describes andillustrates an of a Terminator Library to Improve an application of theSTOP swap Industrial Host Strain genetic design libraries of the presentdisclosure. 9 Comparing HTP Toolsets vs. Traditional UV Providesexperimental results Mutations. comparing the HTP genetic design methodsof the present disclosure vs. traditional mutational strain improvementprograms. 10 Application of HTP Engineering Methods in Describesembodiments of the high Eukaryotes throughput genetic engineeringmethods of the present disclosure, as applied to eukaryotic host cells.11 HTP Genomic Engineering-Implementation Describes approaches for of anHTP SNP Library Strain Improvement rehabilitating industrial Program toImprove Citric Acid production in eukaryotic organisms through EukaryoteAspergillus niger ATCC 11414 SNP swap methods of the present disclosure.

Example 1: HTP Transformation of Corynebacterium & Demonstration of SNPLibrary Creation

This example illustrates embodiments of the HTP genetic engineeringmethods of the present disclosure. Host cells are transformed with avariety of SNP sequences of different sizes, all targeting differentareas of the genome. The results demonstrate that the methods of thepresent disclosure are able to generate rapid genetic changes of anykind, across the entire genome of a host cell.

A. Cloning of Transformation Vectors

A variety of SNPs were chosen at random from Corynebacterium glutamicum(ATCC21300) and were cloned into Corynebacterium cloning vectors usingyeast homologous recombination cloning techniques to assemble a vectorin which each SNP was flanked by direct repeat regions, as describedsupra in the “Assembling/Cloning Custom Plasmids” section, and asillustrated in FIG. 3.

The SNP cassettes for this example were designed to include a range ofhomology direct repeat arm lengths ranging from 0.5 Kb, 1 Kb, 2 Kb, and5 Kb. Moreover, SNP cassettes were designed for homologous recombinationtargeted to various distinct regions of the genome, as described in moredetail below.

The C. glutamicum genome is 3,282,708 bp in size (see FIG. 9). Thegenome was arbitrarily divided into 24 equal-sized genetic regions, andSNP cassettes were designed to target each of the 24 regions. Thus, atotal of 96 distinct plasmids were cloned for this Example (4 differentinsert sizes×24 distinct genomic regions).

Each DNA insert was produced by PCR amplification of homologous regionsusing commercially sourced oligos and the host strain genomic DNAdescribed above as template. The SNP to be introduced into the genomewas encoded in the oligo tails. PCR fragments were assembled into thevector backbone using homologous recombination in yeast.

Cloning of each SNP and homology arm into the vector was conductedaccording to the HTP engineering workflow described in FIG. 6, FIG. 3,and Table 5.

B. Transformation of Assembled Clones into E. coli

Vectors were initially transformed into E. coli using standard heatshock transformation techniques in order to identify correctly assembledclones, and to amplify vector DNA for Corynebacterium transformation.

For example, transformed E. coli bacteria were tested for assemblysuccess. Four colonies from each E. coli transformation plate werecultured and tested for correct assembly via PCR. This process wasrepeated for each of the 24 transformation locations and for each of the4 different insert sizes (i.e., for all 96 transformants of thisexample). Results from this experiment were represented as the number ofcorrect colonies identified out of the four colonies that were testedfor each treatment (insert size and genomic location) (see FIG. 12).Longer 5 kb inserts exhibited a decrease in assembly efficiency comparedto shorter counterparts (n=96).

C. Transformation of Assembled Clones into Corynebacterium

Validated clones were transformed into Corynebacterium glutamicum hostcells via electroporation. For each transformation, the number of ColonyForming Units (CFUs) per μg of DNA was determined as a function of theinsert size (see FIG. 13). Coryne genome integration was also analyzedas a function of homology arm length, and the results showed thatshorter arms had a lower efficiency (see FIG. 13).

Genomic integration efficiency was also analyzed with respect to thetargeted genome location in C. glutamicum transformants. Genomicpositions 1 and 2 exhibited slightly lowered integration efficiencycompared to the rest of the genome (see FIG. 10).

D. Looping Out Selection Markers

Cultures of Corynebacterium identified as having successful integrationsof the insert cassette were cultured on media containing 5% sucrose tocounter select for loop outs of the sacb selection gene. Sucroseresistance frequency for various homology direct repeat arms did notvary significantly with arm length (see FIG. 14). These resultssuggested that loopout efficiencies remained steady across homology armlengths of 0.5 kb to 5 kb.

In order to further validate loop out events, colonies exhibitingsucrose resistance were cultured and analyzed via sequencing.

The results for the sequencing of the insert genomic regions aresummarized in Table 6 below.

TABLE 6 Loop-out Validation Frequency Outcome Frequency (sampling error95% confidence) Successful 13% (9%/20%)  Loop out Loop Still 42%(34%/50%) present Mixed read 44% (36%/52%)

Sequencing results showed a 10-20% efficiency in loop outs. Actualloop-out probably is somewhat dependent on insert sequence. However,picking 10-20 sucrose-resistant colonies leads to high success rates.

E. Summary

Table 7 below provides a quantitative assessment of the efficiencies ofthe HTP genome engineering methods of the present invention. Constructassembly rates for yeast homology methodologies yielded expected DNAconstructs in nearly 9 out of 10 tested colonies. Coryne transformationsof SNP constructs with 2 kb homology arms yielded an average of 51colony forming units per micro gram of DNA (CFU/μg), with 98% of saidcolonies exhibiting correctly integrated SNP inserts (targetingefficiency). Loop out efficiencies remained at 0.2% of cells becomingresistant when exposed to sucrose, with 13% of these exhibitingcorrectly looped out sequences.

TABLE 7 Summary Results for Corynebacterium glutamicum StrainEngineering Results for 2 kb QC Step Homology Arms Construct AssemblySuccess 87% Coryne Transformation efficiency 51 CFU/μg DNA (+/− 15)Targeting efficiency 98% Loop out Efficiency 0.2% (+/− 0.03%)

Example 2: HTP Genomic Engineering—Implementation of a SNP Library toRehabilitate/Improve an Industrial Microbial Strain

This example illustrates several aspects of the SNP swap libraries ofthe HTP strain improvement programs of the present disclosure.Specifically, the example illustrates several envisioned approaches forrehabilitating currently existing industrial strains. This exampledescribes the wave up and wave down approaches to exploring thephenotypic solution space created by the multiple genetic differencesthat may be present between “base,” “intermediate,” and industrialstrains.

A. Identification of SNPs in Diversity Pool

An exemplary strain improvement program using the methods of the presentdisclosure was conducted on an industrial production microbial strain,herein referred to as “C.” The diversity pool strains for this programare represented by A, B, and C. Strain A represented the originalproduction host strain, prior to any mutagenesis. Strain C representedthe current industrial strain, which has undergone many years ofmutagenesis and selection via traditional strain improvement programs.Strain B represented a “middle ground” strain, which had undergone somemutagenesis, and had been the predecessor of strain C. (see FIG. 17A).

Strains A, B, and C were sequenced and their genomes were analyzed forgenetic differences between strains. A total of 332 non-synonymous SNPswere identified. Of these, 133 SNPs were unique to C, 153 wereadditionally shared by B and C, and 46 were unique to strain B (see FIG.17B). These SNPs will be used as the diversity pool for downstreamstrain improvement cycles.

B. SNP Swapping Analysis

SNPs identified from the diversity pool in Part A of Example 2 will beanalyzed to determine their effect on host cell performance. The initial“learning” round of the strain performance will be broken down into sixsteps as described below, and diagramed in FIG. 18.

First, all the SNPs from C will be individually and/or combinatoriallycloned into the base A strain. This will represent a minimum of 286individual transformants. The purpose of these transformants will be toidentify beneficial SNPs.

Second, all the SNPs from C will be individually and/or combinatoriallyremoved from the commercial strain C. This will represent a minimum of286 individual transformants. The purpose of these transformants will beto identify neutral and detrimental SNPs. Additional optional steps 3-6are also described below. The first and second steps of adding andsubtracting SNPS from two genetic time points (base strain A, andindustrial strain C) is herein referred to as “wave,” which comprises a“wave up” (addition of SNPs to a base strain, first step), and a “wavedown” (removal of SNPs from the industrial strain, second step). Thewave concept extends to further additions/subtractions of SNPS.

Third, all the SNPs from B will be individually and/or combinatoriallycloned into the base A strain. This will represent a minimum of 199individual transformants. The purpose of these transformants will be toidentify beneficial SNPs. Several of the transformants will also serveas validation data for transformants produced in the first step.

Fourth, all the SNPs from B will be individually and/or combinatoriallyremoved from the commercial strain B. This will represent a minimum of199 individual transformants. The purpose of these transformants will beto identify neutral and detrimental SNPs. Several of the transformantswill also serve as validation data for transformants produced in thesecond step.

Fifth, all the SNPs unique to C (i.e., not also present in B) will beindividually and/or combinatorially cloned into the commercial B strain.This will represent a minimum of 46 individual transformants. Thepurpose of these transformants will be to identify beneficial SNPs.Several of the transformants will also serve as validation data fortransformants produced in the first and third steps.

Sixth, all the SNPs unique to C will be individually and/orcombinatorially removed from the commercial strain C. This willrepresent a minimum of 46 individual transformants. The purpose of thesetransformants will be to identify neutral and detrimental SNPs. Severalof the transformants will also serve as validation data fortransformants produced in the second and fourth steps.

Data collected from each of these steps is used to classify each SNP asprima facie beneficial, neutral, or detrimental.

C. Utilization of Epistatic Mapping to Determine Beneficial SNPCombinations

Beneficial SNPs identified in Part B of Example 2 will be analyzed viathe epistasis mapping methods of the present disclosure, in order toidentify SNPs that are likely to improve host performance when combined.

New engineered strain variants will be created using the engineeringmethods of Example 1 to test SNP combinations according to epistasismapping predictions. SNPs consolidation may take place sequentially, ormay alternatively take place across multiple branches such that morethan one improved strain may exist with a subset of beneficial SNPs. SNPconsolidation will continue over multiple strain improvement rounds,until a final strain is produced containing the optimum combination ofbeneficial SNPs, without any of the neutral or detrimental SNP baggage

Example 3: HTP Genomic Engineering—Implementation of a SNP Swap Libraryto Improve Strain Performance in Lysine Production in Corynebacterium

This example provides an illustrative implementation of a portion of theSNP Swap HTP design strain improvement program of Example 2 with thegoal of producing yield and productivity improvements of lysineproduction in Corynebacterium.

Section B of this example further illustrates the mutation consolidationsteps of the HTP strain improvement program of the present disclosure.The example thus provides experimental results for a first, second, andthird round consolidation of the HTP strain improvement methods of thepresent disclosure.

Mutations for the second and third round consolidations are derived fromseparate genetic library swaps. These results thus also illustrate theability for the HTP strain programs to be carried out multi-branchparallel tracks, and the “memory” of beneficial mutations that can beembedded into meta data associated with the various forms of the geneticdesign libraries of the present disclosure.

As described above, the genomes of a provided base reference strain(Strain A), and a second “engineered” strain (Strain C) were sequenced,and all genetic differences were identified. The base strain was aCorynebacterium glutamicum variant that had not undergone UVmutagenesis. The engineered strain was also a C. glutamicum strain thathad been produced from the base strain after several rounds oftraditional mutation improvement programs. This Example provides the SNPSwap results for 186 distinct non-synonymous SNP differences identifiedbetween strains A and C.

A. HTP engineering and High Throughput Screening

Each of the 186 identified SNPs were individually added back into thebase strain, according to the cloning and transformation methods of thepresent disclosure. Each newly created strain comprising a single SNPwas tested for lysine yield in small scale cultures designed to assessproduct titer performance. Small scale cultures were conducted usingmedia from industrial scale cultures. Product titer was opticallymeasured at carbon exhaustion (i.e., representative of single batchyield) with a standard colorimetric assay. Briefly, a concentrated assaymixture was prepared and was added to fermentation samples such thatfinal concentrations of reagents were 160 mM sodium phosphate buffer,0.2 mM Amplex Red, 0.2 U/mL Horseradish Peroxidase and 0.005 U/mL oflysine oxidase. Reactions were allowed to proceed to an end point andoptical density measured using a Tecan M1000 plate spectrophotometer ata 560 nm wavelength. The results of the experiment are summarized inTable 8 below, and depicted in FIG. 38.

TABLE 8 Summary Results for SNP Swap Strain Engineering for LysineProduction Mean Lysine Yield (change in A₅₆₀ % Change % compared toreference Std over Change SNP N strain) Error Reference error DSS_033 40.1062 0.00888 11.54348 2.895652 DSS_311 2 0.03603 0.01256 3.9163044.095652 DSS_350 1 0.03178 0.01777 3.454348 5.794565 DSS_056 3 0.026840.01026 2.917391 3.345652 DSS_014 4 0.02666 0.00888 2.897826 2.895652DSS_338 3 0.02631 0.01026 2.859783 3.345652 DSS_128 1 0.02584 0.017772.808696 5.794565 DSS_038 4 0.02467 0.00888 2.681522 2.895652 DSS_066 40.02276 0.00888 2.473913 2.895652 DSS_108 2 0.02216 0.01256 2.4086964.095652 DSS_078 4 0.02169 0.00888 2.357609 2.895652 DSS_017 3 0.021020.01026 2.284783 3.345652 DSS_120 3 0.01996 0.01026 2.169565 3.345652DSS_064 4 0.01889 0.00888 2.053261 2.895652 DSS_380 4 0.01888 0.008882.052174 2.895652 DSS_105 3 0.0184 0.01026 2 3.345652 DSS_407 1 0.018310.01777 1.990217 5.794565 DSS_018 2 0.01825 0.01256 1.983696 4.095652DSS_408 3 0.01792 0.01026 1.947826 3.345652 DSS_417 3 0.01725 0.010261.875 3.345652 DSS_130 3 0.01724 0.01026 1.873913 3.345652 DSS_113 40.0172 0.00888 1.869565 2.895652 DSS_355 3 0.01713 0.01026 1.8619573.345652 DSS_121 3 0.01635 0.01026 1.777174 3.345652 DSS_097 2 0.01620.01256 1.76087 4.095652 DSS_107 3 0.01604 0.01026 1.743478 3.345652DSS_110 2 0.01524 0.01256 1.656522 4.095652 DSS_306 4 0.01501 0.008881.631522 2.895652 DSS_316 1 0.01469 0.01777 1.596739 5.794565 DSS_325 40.01436 0.00888 1.56087 2.895652 DSS_016 4 0.01416 0.00888 1.539132.895652 DSS_324 4 0.01402 0.00888 1.523913 2.895652 DSS_297 4 0.013910.00888 1.511957 2.895652 DSS_118 2 0.01371 0.01256 1.490217 4.095652DSS_100 2 0.01326 0.01256 1.441304 4.095652 DSS_019 1 0.01277 0.017771.388043 5.794565 DSS_131 3 0.01269 0.01026 1.379348 3.345652 DSS_394 40.01219 0.00888 1.325 2.895652 DSS_385 3 0.01192 0.01026 1.2956523.345652 DSS_395 1 0.01162 0.01777 1.263043 5.794565 DSS_287 4 0.011170.00888 1.21413 2.895652 DSS_418 2 0.01087 0.01256 1.181522 4.095652DSS_290 3 0.01059 0.01026 1.151087 3.345652 DSS_314 2 0.01036 0.012561.126087 4.095652 DSS_073 4 0.00986 0.00888 1.071739 2.895652 DSS_040 40.00979 0.00888 1.06413 2.895652 DSS_037 4 0.00977 0.00888 1.0619572.895652 DSS_341 1 0.00977 0.01777 1.061957 5.794565 DSS_302 4 0.009390.00888 1.020652 2.895652 DSS_104 4 0.00937 0.00888 1.018478 2.895652DSS_273 2 0.00915 0.01256 0.994565 4.095652 DSS_322 4 0.00906 0.008880.984783 2.895652 DSS_271 3 0.00901 0.01026 0.979348 3.345652 DSS_334 20.00898 0.01256 0.976087 4.095652 DSS_353 4 0.00864 0.00888 0.939132.895652 DSS_391 4 0.00764 0.00888 0.830435 2.895652 DSS_372 1 0.007370.01777 0.801087 5.794565 DSS_007 1 0.00729 0.01777 0.792391 5.794565DSS_333 2 0.0072 0.01256 0.782609 4.095652 DSS_402 4 0.00718 0.008880.780435 2.895652 DSS_084 1 0.0069 0.01777 0.75 5.794565 DSS_103 30.00676 0.01026 0.734783 3.345652 DSS_362 1 0.00635 0.01777 0.6902175.794565 DSS_012 2 0.00595 0.01256 0.646739 4.095652 DSS_396 2 0.005740.01256 0.623913 4.095652 DSS_133 3 0.00534 0.01026 0.580435 3.345652DSS_065 3 0.00485 0.01026 0.527174 3.345652 DSS_284 2 0.00478 0.012560.519565 4.095652 DSS_301 3 0.00465 0.01026 0.505435 3.345652 DSS_281 40.00461 0.00888 0.501087 2.895652 DSS_405 2 0.00449 0.01256 0.4880434.095652 DSS_361 3 0.00438 0.01026 0.476087 3.345652 DSS_342 4 0.004340.00888 0.471739 2.895652 DSS_053 3 0.00422 0.01026 0.458696 3.345652DSS_074 4 0.00422 0.00888 0.458696 2.895652 DSS_079 4 0.00375 0.008880.407609 2.895652 DSS_381 3 0.0036 0.01026 0.391304 3.345652 DSS_294 10.00336 0.01777 0.365217 5.794565 DSS_313 2 0.00332 0.01256 0.360874.095652 DSS_388 2 0.00305 0.01256 0.331522 4.095652 DSS_392 4 0.002870.00888 0.311957 2.895652 DSS_319 4 0.00282 0.00888 0.306522 2.895652DSS_310 4 0.00263 0.00888 0.28587 2.895652 DSS_344 3 0.00259 0.010260.281522 3.345652 DSS_025 4 0.00219 0.00888 0.238043 2.895652 DSS_412 10.00204 0.01777 0.221739 5.794565 DSS_300 3 0.00188 0.01026 0.2043483.345652 DSS_299 2 0.00185 0.01256 0.201087 4.095652 DSS_343 4 0.001840.00888 0.2 2.895652 DSS_330 3 0.00153 0.01026 0.166304 3.345652 DSS_4164 0.00128 0.00888 0.13913 2.895652 DSS_034 3 0.00128 0.01026 0.139133.345652 DSS_291 2 0.00102 0.01256 0.11087 4.095652 DSS_115 4 0.000630.00888 0.068478 2.895652 DSS_288 4 0.00044 0.00888 0.047826 2.895652DSS_309 4 0.00008 0.00888 0.008696 2.895652 DSS_125 3 0 0.01026 03.345652 DSS_358 3 −0.00015 0.01026 −0.0163 3.345652 DSS_099 2 −0.000150.01256 −0.0163 4.095652 DSS_111 4 −0.00017 0.00888 −0.01848 2.895652DSS_359 3 −0.00022 0.01026 −0.02391 3.345652 DSS_015 4 −0.00043 0.00888−0.04674 2.895652 DSS_060 3 −0.0007 0.01026 −0.07609 3.345652 DSS_098 2−0.00088 0.01256 −0.09565 4.095652 DSS_379 4 −0.00089 0.00888 −0.096742.895652 DSS_356 4 −0.0009 0.00888 −0.09783 2.895652 DSS_278 4 −0.000950.00888 −0.10326 2.895652 DSS_368 4 −0.001 0.00888 −0.1087 2.895652DSS_351 1 −0.0015 0.01777 −0.16304 5.794565 DSS_296 1 −0.0015 0.01777−0.16304 5.794565 DSS_119 3 −0.00156 0.01026 −0.16957 3.345652 DSS_307 3−0.00163 0.01026 −0.17717 3.345652 DSS_077 4 −0.00167 0.00888 −0.181522.895652 DSS_030 3 −0.00188 0.01026 −0.20435 3.345652 DSS_370 2 −0.001890.01256 −0.20543 4.095652 DSS_375 2 −0.00212 0.01256 −0.23043 4.095652DSS_280 3 −0.00215 0.01026 −0.2337 3.345652 DSS_345 4 −0.00225 0.00888−0.24457 2.895652 DSS_419 1 −0.00234 0.01777 −0.25435 5.794565 DSS_298 2−0.00249 0.01256 −0.27065 4.095652 DSS_367 3 −0.0026 0.01026 −0.282613.345652 DSS_072 3 −0.00268 0.01026 −0.2913 3.345652 DSS_366 4 −0.002720.00888 −0.29565 2.895652 DSS_063 4 −0.00283 0.00888 −0.30761 2.895652DSS_092 3 −0.00292 0.01026 −0.31739 3.345652 DSS_347 4 −0.0033 0.00888−0.3587 2.895652 DSS_114 4 −0.0034 0.00888 −0.36957 2.895652 DSS_303 3−0.00396 0.01026 −0.43043 3.345652 DSS_276 4 −0.00418 0.00888 −0.454352.895652 DSS_083 1 −0.00446 0.01777 −0.48478 5.794565 DSS_031 2 −0.004560.01256 −0.49565 4.095652 DSS_328 3 −0.00463 0.01026 −0.50326 3.345652DSS_039 4 −0.00475 0.00888 −0.5163 2.895652 DSS_331 4 −0.00475 0.00888−0.5163 2.895652 DSS_117 4 −0.00485 0.00888 −0.52717 2.895652 DSS_382 4−0.00506 0.00888 −0.55 2.895652 DSS_323 4 −0.00507 0.00888 −0.551092.895652 DSS_041 2 −0.00527 0.01256 −0.57283 4.095652 DSS_069 4 −0.005340.00888 −0.58043 2.895652 DSS_308 3 −0.00534 0.01026 −0.58043 3.345652DSS_365 3 −0.00536 0.01026 −0.58261 3.345652 DSS_403 3 −0.00594 0.01026−0.64565 3.345652 DSS_376 1 −0.00648 0.01777 −0.70435 5.794565 DSS_293 3−0.00652 0.01026 −0.7087 3.345652 DSS_286 1 −0.00672 0.01777 −0.730435.794565 BS.2C 139 −0.00694 0.00151 −0.75435 0.492391 DSS_410 1 −0.007240.01777 −0.78696 5.794565 DSS_312 2 −0.00725 0.01256 −0.78804 4.095652DSS_336 1 −0.00747 0.01777 −0.81196 5.794565 DSS_327 2 −0.00748 0.01256−0.81304 4.095652 DSS_127 4 −0.00801 0.00888 −0.87065 2.895652 DSS_332 3−0.0085 0.01026 −0.92391 3.345652 DSS_054 2 −0.00887 0.01256 −0.964134.095652 DSS_024 2 −0.00902 0.01256 −0.98043 4.095652 DSS_106 3 −0.00960.01026 −1.04348 3.345652 DSS_400 4 −0.00964 0.00888 −1.04783 2.895652DSS_346 3 −0.00976 0.01026 −1.06087 3.345652 DSS_320 1 −0.01063 0.01777−1.15543 5.794565 DSS_275 4 −0.01066 0.00888 −1.1587 2.895652 DSS_371 3−0.01111 0.01026 −1.20761 3.345652 DSS_277 1 −0.01315 0.01777 −1.429355.794565 DSS_282 3 −0.01326 0.01026 −1.4413 3.345652 DSS_393 3 −0.013790.01026 −1.49891 3.345652 DSS_378 3 −0.01461 0.01026 −1.58804 3.345652DSS_289 3 −0.01563 0.01026 −1.69891 3.345652 DSS_317 1 −0.01565 0.01777−1.70109 5.794565 DSS_062 4 −0.01626 0.00888 −1.76739 2.895652 DSS_340 1−0.01657 0.01777 −1.80109 5.794565 DSS_109 2 −0.01706 0.01256 −1.854354.095652 DSS_011 2 −0.0178 0.01256 −1.93478 4.095652 DSS_089 4 −0.018440.00888 −2.00435 2.895652 DSS_059 1 −0.01848 0.01777 −2.0087 5.794565DSS_112 2 −0.01959 0.01256 −2.12935 4.095652 DSS_043 2 −0.0213 0.01256−2.31522 4.095652 DSS_413 1 −0.02217 0.01777 −2.40978 5.794565 DSS_305 4−0.0227 0.00888 −2.46739 2.895652 DSS_045 4 −0.02289 0.00888 −2.488042.895652 DSS_082 2 −0.0231 0.01256 −2.51087 4.095652 DSS_272 1 −0.023110.01777 −2.51196 5.794565 DSS_390 4 −0.02319 0.00888 −2.52065 2.895652DSS_010 3 −0.02424 0.01026 −2.63478 3.345652 DSS_357 2 −0.02525 0.01256−2.74457 4.095652 DSS_085 4 −0.03062 0.00888 −3.32826 2.895652 DSS_044 3−0.04088 0.01026 −4.44348 3.345652 DSS_315 2 −0.0501 0.01256 −5.445654.095652 DSS_080 2 −0.13519 0.01256 −14.6946 4.095652B. Second Round HTP Engineering and High ThroughputScreening—Consolidation of SNP Swap Library with Selected PRO Swap Hits

One of the strengths of the HTP methods of the present disclosure istheir ability to store HTP genetic design libraries together withinformation associated with each SNP/Promoter/Terminator/Start Codon'seffects on host cell phenotypes. The present inventors had previouslyconducted a promoter swap experiment that had identified several zwfpromoter swaps in C. glutamicum with positive effects on biosyntheticyields (see e.g., results for target “N” in FIG. 22).

The present inventors modified the base strain A of this Example to alsoinclude one of the previously identified zwf promoter swaps from Example5. The top 176 SNPs identified from the initial screen described abovein Table 8 were re-introduced into this new base strain to create a newSNP swap genetic design microbial library. As with the previous step,each newly created strain comprising a single SNP was tested for lysineyield. Selected SNP mutant strains were also tested for a productivityproxy, by measuring lysine production at 24 hours using the colorimetricmethod described supra. The results from this step are summarized inTable 9 below, and are depicted in FIG. 39.

TABLE 9 Second Round Screening for SNP Swap Strain Engineering forLysine Production Mean Mean N for N for 24 hr 96 hr Std Error Std ErrorStrain ID SNP 24 hr 96 hr (A₅₆₀) (A₅₆₀) 24 hr 96 hr 7000006318BS2C_P0007_39zwf 20 2 0.49 0.82 0.00 0.02 7000008538 DSS_002 4 2 0.530.78 0.01 0.02 7000008539 DSS_003 4 0.56 0.01 7000008541 DSS_005 4 0.270.01 7000008542 DSS_006 4 0.49 0.01 7000008547 DSS_011 4 0.55 0.017000008548 DSS_012 4 0.58 0.01 7000008549 DSS_013 4 0.56 0.01 7000008550DSS_014 4 0.52 0.01 7000008551 DSS_015 4 0.54 0.01 7000008552 DSS_016 42 0.50 0.84 0.01 0.02 7000008553 DSS_017 4 0.44 0.01 7000008555 DSS_0194 4 0.46 0.84 0.01 0.01 7000008557 DSS_021 4 4 0.46 0.86 0.01 0.017000008559 DSS_023 4 2 0.55 0.86 0.01 0.02 7000008561 DSS_025 4 0.540.01 7000008562 DSS_026 2 0.46 0.01 7000008564 DSS_028 4 0.51 0.017000008565 DSS_029 4 4 0.48 0.87 0.01 0.01 7000008566 DSS_030 4 4 0.470.85 0.01 0.01 7000008567 DSS_031 4 0.56 0.01 7000008569 DSS_033 4 40.46 0.86 0.01 0.01 7000008570 DSS_034 2 2 0.53 0.85 0.01 0.027000008573 DSS_037 4 0.54 0.01 7000008574 DSS_038 4 0.53 0.01 7000008575DSS_039 4 0.55 0.01 7000008576 DSS_040 4 0.57 0.01 7000008577 DSS_041 40.45 0.01 7000008578 DSS_042 4 4 0.52 0.87 0.01 0.01 7000008579 DSS_0434 4 0.45 0.87 0.01 0.01 7000008580 DSS_044 4 2 0.50 0.85 0.01 0.027000008581 DSS_045 4 0.47 0.01 7000008582 DSS_046 4 2 0.61 0.85 0.010.02 7000008583 DSS_047 4 2 0.61 0.82 0.01 0.02 7000008586 DSS_050 40.57 0.01 7000008587 DSS_051 4 0.56 0.01 7000008588 DSS_052 4 2 0.490.85 0.01 0.02 7000008589 DSS_053 4 4 0.45 0.85 0.01 0.01 7000008590DSS_054 4 4 0.45 0.88 0.01 0.01 7000008592 DSS_056 4 0.42 0.017000008596 DSS_060 4 2 0.55 0.87 0.01 0.02 7000008597 DSS_061 4 2 0.370.86 0.01 0.02 7000008598 DSS_062 4 4 0.45 0.87 0.01 0.01 7000008601DSS_065 4 4 0.47 0.88 0.01 0.01 7000008602 DSS_066 4 0.47 0.017000008604 DSS_068 2 0.51 0.02 7000008605 DSS_069 4 4 0.47 0.88 0.010.01 7000008606 DSS_070 4 0.55 0.01 7000008607 DSS_071 4 2 0.56 0.840.01 0.02 7000008608 DSS_072 4 2 0.54 0.83 0.01 0.02 7000008609 DSS_0734 2 0.47 0.84 0.01 0.02 7000008610 DSS_074 4 2 0.51 0.83 0.01 0.027000008612 DSS_076 4 4 0.48 0.76 0.01 0.01 7000008613 DSS_077 4 4 0.460.87 0.01 0.01 7000008614 DSS_078 4 2 0.44 0.87 0.01 0.02 7000008615DSS_079 4 2 0.47 0.90 0.01 0.02 7000008616 DSS_080 4 2 0.48 0.81 0.010.02 7000008619 DSS_083 4 2 0.59 0.86 0.01 0.02 7000008620 DSS_084 4 20.70 0.89 0.01 0.02 7000008621 DSS_085 4 4 0.49 0.89 0.01 0.017000008622 DSS_086 4 2 0.48 0.82 0.01 0.02 7000008624 DSS_088 4 2 0.470.88 0.01 0.02 7000008625 DSS_089 4 4 0.45 0.89 0.01 0.01 7000008626DSS_090 4 4 0.47 0.87 0.01 0.01 7000008627 DSS_091 4 0.46 0.017000008629 DSS_093 4 4 0.50 0.87 0.01 0.01 7000008630 DSS_094 4 2 0.570.86 0.01 0.02 7000008634 DSS_098 4 2 0.53 0.85 0.01 0.02 7000008636DSS_100 4 0.52 0.01 7000008637 DSS_101 4 2 0.49 0.85 0.01 0.027000008640 DSS_104 4 2 0.51 0.84 0.01 0.02 7000008645 DSS_109 4 0.510.01 7000008646 DSS_110 4 2 0.57 0.86 0.01 0.02 7000008648 DSS_112 4 20.54 0.86 0.01 0.02 7000008651 DSS_115 4 0.49 0.01 7000008652 DSS_116 42 0.52 0.82 0.01 0.02 7000008653 DSS_117 4 2 0.50 0.84 0.01 0.027000008657 DSS_121 4 2 0.78 0.88 0.01 0.02 7000008659 DSS_123 4 0.540.01 7000008663 DSS_127 4 0.58 0.01 7000008665 DSS_129 4 0.48 0.017000008666 DSS_130 4 0.56 0.01 7000008669 DSS_133 4 0.50 0.01 7000008670DSS_271 4 2 0.52 0.86 0.01 0.02 7000008672 DSS_273 4 0.56 0.017000008677 DSS_278 2 0.46 0.01 7000008678 DSS_279 4 0.55 0.01 7000008681DSS_282 4 0.51 0.01 7000008683 DSS_284 4 0.59 0.01 7000008684 DSS_285 40.51 0.01 7000008685 DSS_286 4 0.56 0.01 7000008687 DSS_288 4 0.46 0.017000008688 DSS_289 4 0.57 0.01 7000008689 DSS_290 4 0.47 0.01 7000008693DSS_294 4 2 0.52 0.63 0.01 0.02 7000008696 DSS_297 4 2 0.52 0.86 0.010.02 7000008697 DSS_298 4 0.58 0.01 7000008699 DSS_300 4 0.48 0.017000008700 DSS_301 4 0.58 0.01 7000008701 DSS_302 4 0.47 0.01 7000008702DSS_303 3 0.46 0.01 7000008703 DSS_304 3 0.48 0.01 7000008705 DSS_306 42 0.53 0.80 0.01 0.02 7000008708 DSS_309 4 0.56 0.01 7000008709 DSS_3104 0.56 0.01 7000008711 DSS_312 4 0.55 0.01 7000008712 DSS_313 4 0.510.01 7000008718 DSS_319 4 2 0.50 0.82 0.01 0.02 7000008720 DSS_321 40.56 0.01 7000008722 DSS_323 2 2 0.48 0.85 0.01 0.02 7000008723 DSS_3244 0.55 0.01 7000008724 DSS_325 4 0.50 0.01 7000008725 DSS_326 3 0.460.01 7000008726 DSS_327 3 0.47 0.01 7000008730 DSS_331 4 0.56 0.017000008731 DSS_332 4 4 0.47 0.89 0.01 0.01 7000008732 DSS_333 4 4 0.470.87 0.01 0.01 7000008733 DSS_334 4 0.45 0.01 7000008734 DSS_335 2 0.470.01 7000008735 DSS_336 4 0.47 0.01 7000008739 DSS_340 4 0.46 0.017000008740 DSS_341 4 2 0.46 0.89 0.01 0.02 7000008741 DSS_342 4 0.560.01 7000008742 DSS_343 4 0.55 0.01 7000008743 DSS_344 4 4 0.48 0.870.01 0.01 7000008746 DSS_347 4 4 0.48 0.85 0.01 0.01 7000008747 DSS_3484 4 0.46 0.86 0.01 0.01 7000008749 DSS_350 4 2 0.29 0.74 0.01 0.027000008752 DSS_353 4 2 0.46 0.85 0.01 0.02 7000008753 DSS_354 4 4 0.450.87 0.01 0.01 7000008755 DSS_356 4 4 0.46 0.86 0.01 0.01 7000008756DSS_357 4 4 0.46 0.86 0.01 0.01 7000008758 DSS_359 2 2 0.45 0.85 0.010.02 7000008760 DSS_361 4 2 0.46 0.84 0.01 0.02 7000008761 DSS_362 40.44 0.01 7000008763 DSS_364 4 0.44 0.01 7000008764 DSS_365 4 0.46 0.017000008765 DSS_366 4 0.55 0.01 7000008766 DSS_367 4 0.55 0.01 7000008767DSS_368 4 2 0.44 0.86 0.01 0.02 7000008770 DSS_371 4 2 0.47 0.88 0.010.02 7000008771 DSS_372 4 2 0.46 0.83 0.01 0.02 7000008772 DSS_373 4 20.46 0.88 0.01 0.02 7000008774 DSS_375 4 0.45 0.01 7000008776 DSS_377 40.45 0.01 7000008777 DSS_378 4 0.57 0.01 7000008778 DSS_379 4 0.54 0.017000008779 DSS_380 4 2 0.46 0.87 0.01 0.02 7000008781 DSS_382 4 2 0.460.84 0.01 0.02 7000008782 DSS_383 4 0.48 0.01 7000008783 DSS_384 4 20.47 0.82 0.01 0.02 7000008784 DSS_385 4 2 0.46 0.83 0.01 0.027000008786 DSS_387 3 0.43 0.01 7000008787 DSS_388 3 0.47 0.01 7000008788DSS_389 4 2 0.46 0.89 0.01 0.02 7000008790 DSS_391 4 0.57 0.017000008791 DSS_392 4 0.44 0.01 7000008795 DSS_396 4 2 0.46 0.82 0.010.02 7000008799 DSS_400 4 0.47 0.01 7000008800 DSS_401 4 2 0.46 0.860.01 0.02 7000008801 DSS_402 4 0.54 0.01 7000008805 DSS_406 4 2 0.470.85 0.01 0.02 7000008807 DSS_408 4 0.45 0.01 7000008810 DSS_411 4 20.46 0.87 0.01 0.02 7000008812 DSS_413 3 0.47 0.01 7000008813 DSS_414 42 0.45 0.84 0.01 0.02 7000008815 DSS_416 4 2 0.45 0.87 0.01 0.027000008816 DSS_417 4 0.46 0.01 7000008818 DSS_419 4 2 0.47 0.84 0.010.02 7000008820 DSS_421 4 2 0.45 0.79 0.01 0.02 7000008821 DSS_422 40.44 0.01

The results from this second round of SNP swap identified several SNPscapable of increasing base strain yield and productivity of lysine in abase strain comprising the zwf promoter swap mutation (see e.g., SNP 084and SNP 121 on the upper right hand corner of FIG. 39).

C. Tank Culture Validation

Strains containing top SNPs identified during the HTP steps above werecultured into medium sized test fermentation tanks. Briefly, small 100ml cultures of each strain were grown over night, and were then used toinoculate 5 liter cultures in the test fermentation tanks with equalamounts of inoculate. The inoculate was normalized to contain the samecellular density following an OD600 measurement.

The resulting tank cultures were allowed to proceed for 3 days beforeharvest. Yield and productivity measurements were calculated fromsubstrate and product titers in samples taken from the tank at variouspoints throughout the fermentation. Samples were analyzed for particularsmall molecule concentrations by high pressure liquid chromatographyusing the appropriate standards. Results for this experiment aresummarized in Table 10 below, and depicted in FIG. 40.

TABLE 10 Tank Validation of SNP Swap Microbes Mean Yield (%)(g lysineproduced/ Mean g glucose Std Productivity Std Strain N consumed) Error(g/L/h) Error base strain 1 41.1502 0.59401 3.29377 0.24508 basestrain + zwf 7 48.2952 0.22451 2.73474 0.10005 base strain + zwf + 250.325  0.42003 4.51397 0.1733  SNP121 base strain + zwf + 5 52.191 0.26565 4.15269 0.12254 pyc + lysA

As predicted by the small scale high throughput cultures, larger tankcultures for strains comprising the combined zwf promoter swap and SNP121 exhibited significant increases in yield and productivity over thebase reference strain. Productivity of this strain for example, jumpedto 4.5 g/L/h compared to the 3.29 g/L/h productivity of the base strain(a 37.0% increase in productivity in only 2 rounds of SNP Swap).

Example 4: HTP Genomic Engineering—Implementation of a Promoter SwapLibrary to Improve an Industrial Microbial Strain

Previous examples have demonstrated the power of the HTP strainimprovement programs of the present disclosure for rehabilitatingindustrial strains. Examples 2 and 3 described the implementation of SNPswap techniques and libraries exploring the existing genetic diversitywithin various base, intermediate, and industrial strains

This example illustrates embodiments of the HTP strain improvementprograms using the PRO swap techniques of the present disclosure. UnlikeExample 3, this example teaches methods for the de-novo generation ofmutations via PRO swap library generation.

A. Identification of a Target for Promoter Swapping

As aforementioned, promoter swapping is a multi-step process thatcomprises a step of: Selecting a set of “n” genes to target.

In this example, the inventors have identified a group of 23 potentialpathway genes to modulate via the promoter ladder methods of the presentdisclosure (19 genes to overexpress and 4+diverting genes todownregulate, in an exemplary metabolic pathway producing the moleculelysine). (See, FIG. 19).

B. Creation of Promoter Ladder

Another step in the implementation of a promoter swap process is theselection of a set of “x” promoters to act as a “ladder”. Ideally thesepromoters have been shown to lead to highly variable expression acrossmultiple genomic loci, but the only requirement is that they perturbgene expression in some way.

These promoter ladders, in particular embodiments, are created by:identifying natural, native, or wild-type promoters associated with thetarget gene of interest and then mutating said promoter to derivemultiple mutated promoter sequences. Each of these mutated promoters istested for effect on target gene expression. In some embodiments, theedited promoters are tested for expression activity across a variety ofconditions, such that each promoter variant's activity isdocumented/characterized/annotated and stored in a database. Theresulting edited promoter variants are subsequently organized into“ladders” arranged based on the strength of their expression (e.g., withhighly expressing variants near the top, and attenuated expression nearthe bottom, therefore leading to the term “ladder”).

In the present exemplary embodiment, the inventors have created promoterladder:ORF combinations for each of the target genes identified in FIG.19.

C. Associating Promoters from the Ladder with Target Genes

Another step in the implementation of a promoter swap process is the HTPengineering of various strains that comprise a given promoter from thepromoter ladder associated with a particular target gene.

If a native promoter exists in front of target gene n and its sequenceis known, then replacement of the native promoter with each of the xpromoters in the ladder can be carried out. When the native promoterdoes not exist or its sequence is unknown, then insertion of each of thex promoters in the ladder in front of gene n can be carried out. In thisway a library of strains is constructed, wherein each member of thelibrary is an instance of x promoter operably linked to n target, in anotherwise identical genetic context (see e.g., FIG. 20).

D. HTP Screening of the Strains

A final step in the promoter swap process is the HTP screening of thestrains in the aforementioned library. Each of the derived strainsrepresents an instance of x promoter linked to n target, in an otherwiseidentical genetic background.

By implementing a HTP screening of each strain, in a scenario wheretheir performance against one or more metrics is characterized, theinventors are able to determine what promoter/target gene association ismost beneficial for a given metric (e.g. optimization of production of amolecule of interest). See, FIG. 20 (promoters P1-P8 effect on gene ofinterest).

In the exemplary embodiment illustrated in FIGS. 19-22, the inventorshave utilized the promoter swap process to optimize the production oflysine. An application of the Pro SWAP methods described above isdescribed in Example 5, below.

Example 5: HTP Genomic Engineering—Implementation of a PRO Swap Libraryto Improve Strain Performance for Lysine Production

The section below provides an illustrative implementation of the PROswap HTP design strain improvement program tools of the presentdisclosure, as described in Example 4. In this example, aCorynebacterium strain was subjected to the PRO swap methods of thepresent disclosure in order to increase host cell yield of lysine.

A. Promoter Swap

Promoter Swaps were conducted as described in Example 4. Selected genesfrom the Lysine biosynthetic pathway in FIG. 19 were targeted forpromoter swaps using promoters P1-P8.

B. HTP engineering and High Throughput Screening

HTP engineering of the promoter swaps was conducted as described inExample 1 and 3. HTP screening of the resulting promoter swap strainswas conducted as described in Example 3. In total 145 PRO swaps wereconducted. The results of the experiment are summarized in Table 11below, and are depicted in FIG. 41.

TABLE 11 HTP Screening of Lysine PRO Swap Libraries % Yield ChangeStrain promoter-target N Mean (A₅₆₀) Std Error From Base 7000007713Pcg1860-asd 8 0.84595 0.00689 3.927615 7000007736 Pcg0755-asd 4 0.840360.00974 3.240866 7000007805 Pcg0007_119-asd 8 0.82493 0.00689 1.3452427000007828 Pcg3121-asd 8 0.8246 0.00689 1.3047 7000007759Pcg0007_265-asd 8 0.81155 0.00689 −0.29853 7000007782 Pcg3381-asd 80.8102 0.00689 −0.46438 7000007712 Pcg1860-ask 8 0.83958 0.00689 3.145047000007735 Pcg0755-ask 8 0.81673 0.00689 0.337846 7000007827 Pcg3121-ask8 0.81498 0.00689 0.122853 7000007804 Pcg0007_119-ask 8 0.81492 0.006890.115482 7000007758 Pcg0007_265-ask 8 0.80381 0.00689 −1.249427000007781 Pcg3381-ask 8 0.80343 0.00689 −1.2961 7000007780 Pcg3381-aspB8 0.84072 0.00689 3.285093 7000007803 Pcg0007_119-aspB 8 0.82106 0.006890.8698 7000007809 Pcg0007_119-cg0931 8 0.83446 0.00689 2.5160327000007717 Pcg1860-cg0931 4 0.83129 0.00974 2.126588 7000007763Pcg0007_265-cg0931 4 0.82628 0.00974 1.511094 7000007671Pcg0007_39-cg0931 8 0.82554 0.00689 1.420182 7000007740 Pcg0755-cg0931 80.81921 0.00689 0.642522 7000007694 Pcg0007-cg0931 8 0.80444 0.00689−1.17202 7000007691 Pcg0007-dapA 8 0.8299 0.00689 1.955822 7000007783Pcg3381-dapA 8 0.80951 0.00689 −0.54915 7000007760 Pcg0007_265-dapA 80.76147 0.00689 −6.45102 7000007806 Pcg0007_119-dapA 8 0.35394 0.00689−56.5174 7000007761 Pcg0007_265-dapB 8 0.84157 0.00689 3.3895187000007738 Pcg0755-dapB 4 0.84082 0.00974 3.297378 7000007692Pcg0007-dapB 8 0.83088 0.00689 2.076218 7000007784 Pcg3381-dapB 80.82474 0.00689 1.3219 7000007715 Pcg1860-dapB 8 0.82232 0.006891.024595 7000007830 Pcg3121-dapB 8 0.81236 0.00689 −0.19902 7000007807Pcg0007_119-dapB 4 0.69622 0.00974 −14.4672 7000007762 Pcg0007_265-dapD8 0.84468 0.00689 3.771591 7000007808 Pcg0007_119-dapD 8 0.83869 0.006893.035701 7000007785 Pcg3381-dapD 8 0.83397 0.00689 2.455834 7000007670Pcg0007_39-dapD 8 0.81698 0.00689 0.368559 7000007831 Pcg3121-dapD 40.8155 0.00974 0.186737 7000007693 Pcg0007-dapD 8 0.8117 0.00689−0.28011 7000007716 Pcg1860-dapD 8 0.79044 0.00689 −2.89196 7000007739Pcg0755-dapD 8 0.78694 0.00689 −3.32195 7000007787 Pcg3381-dapE 80.83814 0.00689 2.968132 7000007833 Pcg3121-dapE 8 0.83721 0.006892.853878 7000007741 Pcg0755-dapE 8 0.83263 0.00689 2.291211 7000007810Pcg0007_119-dapE 8 0.83169 0.00689 2.175729 7000007718 Pcg1860-dapE 80.81855 0.00689 0.561439 7000007672 Pcg0007_39-dapE 8 0.80932 0.00689−0.5725 7000007765 Pcg0007_265-dapF 8 0.8327 0.00689 2.299811 7000007788Pcg3381-dapF 8 0.82942 0.00689 1.896853 7000007811 Pcg0007_119-dapF 80.82926 0.00689 1.877196 7000007696 Pcg0007-dapF 8 0.82099 0.006890.861201 7000007719 Pcg1860-dapF 8 0.82067 0.00689 0.821888 7000007673Pcg0007_39-dapF 8 0.82062 0.00689 0.815745 7000007789 Pcg3381-ddh 80.84817 0.00689 4.200349 7000007835 Pcg3121-ddh 8 0.82141 0.006890.912799 7000007812 Pcg0007_119-ddh 8 0.82093 0.00689 0.8538297000007674 Pcg0007_39-ddh 8 0.81494 0.00689 0.117939 7000007720Pcg1860-ddh 8 0.81473 0.00689 0.09214 7000007766 Pcg0007_265-ddh 80.81427 0.00689 0.035627 7000007743 Pcg0755-ddh 8 0.80655 0.00689−0.9128 7000007697 Pcg0007-ddh 8 0.80621 0.00689 −0.95457 7000007779Pcg3381-fbp 8 0.85321 0.00689 4.819529 7000007802 Pcg0007_119-fbp 40.81425 0.00974 0.03317 7000007710 Pcg1860-fbp 4 0.40253 0.00974−50.5479 7000007687 Pcg0007-fbp 8 0.14881 0.00689 −81.7182 7000007825Pcg3121-fbp 4 0.12471 0.00974 −84.679 7000007733 Pcg0755-fbp 4 0.082170.00974 −89.9052 7000007746 Pcg0755-hom 8 0.81925 0.00689 0.6474367000007792 Pcg3381-hom 4 0.77674 0.00974 −4.57505 7000007723 Pcg1860-hom8 0.71034 0.00689 −12.7325 7000007838 Pcg3121-hom 8 0.559 0.00689−31.3251 7000007800 Pcg0007_119-icd 8 0.83236 0.00689 2.2580417000007823 Pcg3121-icd 8 0.83155 0.00689 2.15853 7000007777 Pcg3381-icd8 0.82844 0.00689 1.776456 7000007708 Pcg1860-icd 8 0.82384 0.006891.211332 7000007662 Pcg0007_39-icd 12 0.82008 0.00562 0.7494047000007685 Pcg0007-icd 8 0.81257 0.00689 −0.17322 7000007754Pcg0007_265-icd 4 0.81172 0.00974 −0.27765 7000007698 Pcg0007-lysA 40.8504 0.00974 4.474311 7000007675 Pcg0007_39-lysA 8 0.84414 0.006893.705251 7000007836 Pcg3121-lysA 4 0.83545 0.00974 2.637657 7000007767Pcg0007_265-lysA 8 0.83249 0.00689 2.274012 7000007813 Pcg0007_119-lysA8 0.83096 0.00689 2.086046 7000007790 Pcg3381-lysA 8 0.8118 0.00689−0.26782 7000007676 Pcg0007_39-lysE 8 0.84394 0.00689 3.68068 7000007699Pcg0007-lysE 4 0.83393 0.00974 2.45092 7000007768 Pcg0007_265-lysE 80.83338 0.00689 2.383351 7000007837 Pcg3121-lysE 4 0.83199 0.009742.212585 7000007791 Pcg3381-lysE 8 0.81476 0.00689 0.095825 7000007814Pcg0007_119-lysE 8 0.81315 0.00689 −0.10197 7000007775 Pcg3381-odx 80.82237 0.00689 1.030738 7000007752 Pcg0007_265-odx 8 0.81118 0.00689−0.34399 7000007729 Pcg0755-odx 8 0.81103 0.00689 −0.36242 7000007683Pcg0007-odx 8 0.80507 0.00689 −1.09462 7000007706 Pcg1860-odx 4 0.793320.00974 −2.53815 7000007660 Pcg0007_39-odx 8 0.79149 0.00689 −2.762977000007798 Pcg0007_119-odx 8 0.77075 0.00689 −5.31094 7000007821Pcg3121-odx 4 0.74788 0.00974 v8.12059 7000007822 Pcg3121-pck 8 0.855440.00689 5.093491 7000007776 Pcg3381-pck 8 0.8419 0.00689 3.430067000007799 Pcg0007_119-pck 8 0.83851 0.00689 3.013588 7000007753Pcg0007_265-pck 8 0.82738 0.00689 1.646232 7000007730 Pcg0755-pck 40.81785 0.00974 0.475442 7000007661 Pcg0007_39-pck 8 0.80976 0.00689−0.51844 7000007684 Pcg0007-pck 8 0.79007 0.00689 −2.93742 7000007707Pcg1860-pck 8 0.71566 0.00689 −12.0789 7000007840 Pcg3121-pgi 4 1.010460.00974 24.13819 7000007817 Pcg0007_119-pgi 7 0.99238 0.00736 21.9177000007794 Pcg3381-pgi 7 0.99008 0.00736 21.63444 7000007771Pcg0007_265-pgi 8 0.94665 0.00689 16.29893 7000007725 Pcg1860-pgi 80.85515 0.00689 5.057864 7000007702 Pcg0007-pgi 4 0.8056 0.00974−1.02951 7000007658 Pcg0007_39-ppc 4 0.85221 0.00974 4.696676 7000007750Pcg0007_265-ppc 8 0.84486 0.00689 3.793705 7000007727 Pcg0755-ppc 80.84166 0.00689 3.400575 7000007773 Pcg3381-ppc 4 0.82883 0.009741.824369 7000007796 Pcg0007_119-ppc 8 0.82433 0.00689 1.27153 7000007704Pcg1860-ppc 8 0.81736 0.00689 0.415244 7000007819 Pcg3121-ppc 8 0.798980.00689 −1.8428 7000007732 Pcg0755-ptsG 8 0.84055 0.00689 3.2642087000007709 Pcg1860-ptsG 8 0.81075 0.00689 −0.39682 7000007663Pcg0007_39-ptsG 8 0.80065 0.00689 −1.63763 7000007778 Pcg3381-ptsG 80.23419 0.00689 −71.229 7000007801 Pcg0007_119-ptsG 8 0.17295 0.00689−78.7525 7000007824 Pcg3121-ptsG 8 0.16035 0.00689 −80.3005 7000007705Pcg1860-pyc 8 0.85143 0.00689 4.60085 7000007728 Pcg0755-pyc 8 0.798030.00689 −1.95951 7000007659 Pcg0007_39-pyc 8 0.75539 0.00689 −7.197977000007751 Pcg0007_265-pyc 8 0.73664 0.00689 −9.50146 7000007682Pcg0007-pyc 4 0.73142 0.00974 −10.1428 7000007774 Pcg3381-pyc 4 0.666670.00974 −18.0975 7000007797 Pcg0007_119-pyc 4 0.52498 0.00974 −35.50467000007820 Pcg3121-pyc 8 0.52235 0.00689 −35.8277 7000007841 Pcg3121-tkt8 0.82565 0.00689 1.433696 7000007818 Pcg0007_119-tkt 8 0.81674 0.006890.339075 7000007749 Pcg0755-tkt 8 0.81496 0.00689 0.120396 7000007703Pcg0007-tkt 4 0.76763 0.00974 −5.69424 7000007795 Pcg3381-tkt 8 0.722130.00689 −11.2841 7000007772 Pcg0007_265-tkt 8 0.68884 0.00689 −15.37387000007701 Pcg0007-zwf 4 0.95061 0.00974 16.78542 7000007747 Pcg0755-zwf8 0.92595 0.00689 13.75587 7000007770 Pcg0007_265-zwf 8 0.9029 0.0068910.9241 7000007724 Pcg1860-zwf 8 0.79309 0.00689 −2.5664 7000007839Pcg3121-zwf 4 0.13379 0.00974 −83.5635 7000000017 — 116 0.92115 0.0018113.16617 7000006284 — 128 0.81398 0.00172 0 7000005754 — 64 0.794890.00243 −2.34527

When visualized, the results of the promoter swap library screeningserve to identify gene targets that are most closely correlated with theperformance metric being measured. In this case, gene targets pgi, zwf,ppc, pck, fbp, and ddh were identified as genes for which promoter swapsproduce large gains in yield over base strains.

Selected strains from Table 11 were re-cultured in small plates andtested for lysine yield as describe above. The results from thissecondary screening are provided in FIG. 22.

Example 6: Epistasis Mapping—an Algorithmic Tool for PredictingBeneficial Mutation Consolidations

This example describes an embodiment of the predictive modelingtechniques utilized as part of the HTP strain improvement program of thepresent disclosure. After an initial identification of potentiallybeneficial mutations (through the use of genetic design libraries asdescribed above), the present disclosure teaches methods ofconsolidating beneficial mutations in second, third, fourth, andadditional subsequent rounds of HTP strain improvement. In someembodiments, the present disclosure teaches that mutation consolidationsmay be based on the individual performance of each of said mutations. Inother embodiments, the present disclosure teaches methods for predictingthe likelihood that two or more mutations will exhibit additive orsynergistic effects if consolidated into a single host cell. The examplebelow illustrates an embodiment of the predicting tools of the presentdisclosure.

Selected mutations from the SNP swap and promoter swapping (PRO swap)libraries of Examples 3 and 5 were analyzed to identify SNP/PRO swapcombinations that would be most likely to lead to strain hostperformance improvements.

SNP swapping library sequences were compared to each other using acosine similarity matrix, as described in the “Epistasis Mapping”section of the present disclosure. The results of the analysis yieldedfunctional similarity scores for each SNP/PRO swap combination. A visualrepresentation of the functional similarities among all SNPs/PRO swapsis depicted in a heat map in FIG. 15. The resulting functionalsimilarity scores were also used to develop a dendrogram depicting thesimilarity distance between each of the SNPs/PRO swaps (FIG. 16A).

Mutations from the same or similar functional group (i.e., SNPs/PROswaps with high functional similarity) are more likely to operate by thesame mechanism, and are thus more likely to exhibit negative or neutralepistasis on overall host performance when combined. In contrast,mutations from different functional groups would be more likely tooperate by independent mechanisms, and thus more likely to producebeneficial additive or combinatorial effects on host performance.

In order to illustrate the effects of biological pathways on epistasis,SNPs and PRO swaps exhibiting various functional similarities werecombined and tested on host strains. Three SNP/PRO swap combinationswere engineered into the genome of Corynebacterium glutamicum asdescribed in Example 1: i) Pcg0007::zwf PRO swap+Pcg1860::pyc PRO swap,ii) Pcg0007::zwf PRO swap+SNP 309, and iv) Pcg0007::zwf PROswap+Pcg0007::lysA PRO swap (see FIGS. 15 and 16A for functionalsimilarity relationships).

The performance of each of the host cells containing the SNP/PRO swapcombinations was tested as described in Example 3, and was compared tothat of a control host cell containing only zwf PRO swap. Tables 12 and13 below summarize the results of host cell yield (96 hr measurements)and productivity (24 hr measurements) of each of the strains.

TABLE 12 Lysine Accumulation for Epistasis Mapping Experiment at 24hours. Mean Lysine SNP/PRO swap (A₅₆₀) StDev 6318 (zwf) 0.51 0.03 8126(zwf + lysA) 0.88 0.06 8156 (zwf + pyc) 0.53 0.01 8708 (zwf + SNP 0.560.00 309)

TABLE 13 Lysine Accumulation for Epistasis Mapping Experiment at 96hours. Mean Lysine SNP/PRO swap (A₅₆₀) StDev 6318 (zwf) 0.83 0.01 8126(zwf + lysA) 0.94 0.02 8156 (zwf + pyc) 0.83 0.06

Host yield performance results for each SNP/PRO swap combination arealso depicted in FIG. 16B. Host strains combining SNPs/PRO swapsexhibiting lower functional similarity outperformed strains in which thecombined SNPs had exhibited higher functional similarity at both 24, and96 hour measurements.

Thus, the epistatic mapping procedure is useful forpredicting/programming/informing effective and/or positiveconsolidations of designed genetic changes. The analytical insight fromthe epistatic mapping procedure allows for the creation of predictiverule sets that can guide subsequent rounds of microbial straindevelopment. The predictive insight gained from the epistatic librarymay be used across microbial types and target molecule types.

Example 7: HTP Genomic Engineering—Pro Swap Mutation Consolidation andMulti-Factor Combinatorial Testing

Previous examples have illustrated methods for consolidating a smallnumber of pre-selected PRO swap mutations with SNP swap libraries(Example 3). Other examples have illustrated the epistatic methods forselecting mutation consolidations that are most likely to yield additiveor synergistic beneficial host cell properties (Example 6). This exampleillustrates the ability of the HTP methods of the present disclosure toeffectively explore the large solution space created by thecombinatorial consolidation of multiple gene/genetic design librarycombinations (e.g., PRO swap library×SNP Library or combinations withina PRO swap library).

In this illustrative application of the HTP strain improvement methodsof the present disclosure, promoter swaps identified as having apositive effect on host performance in Example 5 are consolidated insecond order combinations with the original PRO swap library. Thedecision to consolidate PRO swap mutations was based on each mutation'soverall effect on yield or productivity, and the likelihood that thecombination of the two mutations would produce an additive orsynergistic effect.

For example, applicants refer to their choice of combining Pcg0007::zwfand Pcg0007::lysA, based on the epistasis mapping results of Example 6.

A. Consolidation Round for PRO Swap Strain Engineering

Strains were transformed as described in previous Example 1. Briefly,strains already containing one desired PRO swap mutation were once againtransformed with the second desired PRO swap mutation. In total, the 145tested PRO swaps from Example 5 were consolidated into 53 second roundconsolidation strains, each comprising two PRO swap mutations expectedto exhibit beneficial additive or synergistic effects.

The resulting second round strains were once again screened as describedin Example 3. Results from this experiment are summarized in Table 14below, and depicted in FIG. 11.

TABLE 14 HTP Screening of Second Round Consolidated Lysine PRO SwapLibraries Mean Yield Strain ID Number PRO Swap 1 PRO Swap 2 (A₅₆₀) StdDev 7000008489 4 Pcg0007-lysA Pcg3121-pgi 1.17333 0.020121 7000008530 8Pcg1860-pyc Pcg0007-zwf 1.13144 0.030023 7000008491 7 Pcg0007-lysAPcg0007-zwf 1.09836 0.028609 7000008504 8 Pcg3121-pck Pcg0007-zwf1.09832 0.021939 7000008517 8 Pcg0007_39-ppc Pcg0007-zwf 1.095020.030777 7000008502 4 Pcg3121-pck Pcg3121-pgi 1.09366 0.0758547000008478 4 Pcg3381-ddh Pcg0007-zwf 1.08893 0.025505 7000008465 4Pcg0007_265-dapB Pcg0007-zwf 1.08617 0.025231 7000008535 8 Pcg0007-zwfPcg3121-pgi 1.06261 0.019757 7000008476 6 Pcg3381-ddh Pcg3121-pgi1.04808 0.084307 7000008510 8 Pcg3121-pgi Pcg1860-pyc 1.04112 0.0210877000008525 8 Pcg1860-pyc Pcg0007_265-dapB 1.0319 0.034045 7000008527 8Pcg1860-pyc Pcg0007-lysA 1.02278 0.043549 7000008452 5 Pcg1860-asdPcg0007-zwf 1.02029 0.051663 7000008463 4 Pcg0007_265-dapB Pcg3121-pgi1.00511 0.031604 7000008524 8 Pcg1860-pyc Pcg1860-asd 1.00092 0.0263557000008458 4 Pcg3381-aspB Pcg1860-pyc 1.00043 0.020083 7000008484 8Pcg3381-fbp Pcg1860-pyc 0.99686 0.061364 7000008474 8 Pcg3381-ddhPcg3381-fbp 0.99628 0.019733 7000008522 8 Pcg0755-ptsG Pcg3121-pgi0.99298 0.066021 7000008528 8 Pcg1860-pyc Pcg3121-pck 0.99129 0.0215617000008450 4 Pcg1860-asd Pcg3121-pgi 0.98262 0.003107 7000008448 8Pcg1860-asd Pcg3381-fbp 0.97814 0.022285 7000008494 8 Pcg0007_39-lysEPcg3381-fbp 0.97407 0.027018 7000008481 8 Pcg3381-fbp Pcg0007-lysA0.9694 0.029315 7000008497 8 Pcg0007_39-lysE Pcg1860-pyc 0.9678 0.0285697000008507 8 Pcg3121-pgi Pcg3381-fbp 0.96358 0.035078 7000008501 8Pcg3121-pck Pcg0007-lysA 0.96144 0.018665 7000008486 8 Pcg0007-lysAPcg0007_265-dapB 0.94523 0.017578 7000008459 8 Pcg0007_265-dapBPcg1860-asd 0.94462 0.023847 7000008506 2 Pcg3121-pgi Pcg0007_265-dapD0.94345 0.014014 7000008487 8 Pcg0007-lysA Pcg3381-ddh 0.94249 0.0096847000008498 8 Pcg3121-pck Pcg1860-asd 0.94154 0.016802 7000008485 8Pcg0007-lysA Pcg1860-asd 0.94135 0.013578 7000008499 8 Pcg3121-pckPcg0007_265-dapB 0.93805 0.013317 7000008472 8 Pcg3381-ddh Pcg1860-asd0.93716 0.012472 7000008511 8 Pcg0007_39-ppc Pcg1860-asd 0.936730.015697 7000008514 8 Pcg0007_39-ppc Pcg0007-lysA 0.93668 0.0272047000008473 8 Pcg3381-ddh Pcg0007_265-dapB 0.93582 0.030377 7000008461 7Pcg0007_265-dapB Pcg3381-fbp 0.93498 0.037862 7000008512 8Pcg0007_39-ppc Pcg0007_265-dapB 0.93033 0.017521 7000008456 8Pcg3381-aspB Pcg3121-pck 0.92544 0.020075 7000008460 8 Pcg0007_265-dapBPcg0007_265-dapD 0.91723 0.009508 7000008492 8 Pcg0007_39-lysEPcg3381-aspB 0.91165 0.012988 7000008493 8 Pcg0007_39-lysEPcg0007_265-dapD 0.90609 0.031968 7000008453 8 Pcg3381-aspBPcg0007_265-dapB 0.90338 0.013228 7000008447 8 Pcg1860-asdPcg0007_265-dapD 0.89886 0.028896 7000008455 8 Pcg3381-aspB Pcg0007-lysA0.89531 0.027108 7000008454 6 Pcg3381-aspB Pcg3381-ddh 0.87816 0.0258077000008523 8 Pcg0755-ptsG Pcg1860-pyc 0.87693 0.030322 7000008520 8Pcg0755-ptsG Pcg3381-fbp 0.87656 0.018452 7000008533 4 Pcg0007-zwfPcg3381-fbp 0.84584 0.017012 7000008519 8 Pcg0755-ptsG Pcg0007_265-dapD0.84196 0.025747

As predicted by the epistasis model, the second round PRO swap straincomprising the Pcg0007::zwf and Pcg0007::lysA mutations exhibited one ofthe highest yield improvements, with a nearly 30% improvement in yieldover Pcg0007::lysA alone, and a 35.5% improvement over the base strain(see circled data point on FIG. 11).

The HTP methods for exploring solution space of single and doubleconsolidated mutations, can also be applied to third, fourth, andsubsequent mutation consolidations. Attention is also drawn, forexample, to the disclosed 3-change consolidation strain corresponding tozwf, pyc, and lysa that was made from amongst the top hits of identifiedin the 2 change consolidations as shown in Table 14 above, and asidentified by the epistatic methods of the present disclosure. This3-change consolidation strain was further validated in tanks as beingsignificantly improved as compared to the parent or parent+zwf (seeTable 10 supra, and FIG. 40).

Example 8: HTP Genomic Engineering—Implementation of a TerminatorLibrary to Improve an Industrial Host Strain

The present example applies the HTP methods of the present disclosure toadditional HTP genetic design libraries, including STOP swap. Theexample further illustrates the ability of the present disclosure tocombine elements from basic genetic design libraries (e.g., PRO swap,SNP swap, STOP swap, etc.,) to create more complex genetic designlibraries (e.g., PRO-STOP swap libraries, incorporating both a promoterand a terminator). In some embodiments, the present disclosure teachesany and all possible genetic design libraries, including those derivedfrom combining any of the previously disclosed genetic design libraries.

In this example, a small scale experiment was conducted to demonstratethe effect of the STOP swap methods of the present invention on geneexpression. Terminators T₁-T₈ of the present disclosure were paired withone of two native Corynebacterium glutamicum promoters as describedbelow, and were analyzed for their ability to impact expression of afluorescent protein.

A. Assembly of DNA Constructs

Terminators T1-T₈ were paired with one of two native Corynebacteriumglutamicum promoters (e.g., Pcg0007 or Pcg0047) expressing a yellowfluorescence protein (YFP). To facilitate DNA amplification andassembly, the final promoter-YFP-terminator sequence was synthesized intwo portions; the first portion encoded (from 5′ to 3′) 1) the vectorhomology arm, ii) the selected promoter, iii) and ⅔ of the YFP gene. Thesecond portion encoded (from 5′ to 3′) iv) the next ⅔ of the YFP gene,v) the selected terminator, and vi) the second vector homology arm. Eachportion was amplified using synthetic oligonucleotides and gel purified.Gel purified amplicons were assembled with a vector backbone using yeasthomologous recombination.

B. Transformation of Assembled Clones into E. coli

Vectors containing the Promoter-YFP-terminator sequences were eachindividually transformed into E. coli in order to identify correctlyassembled clones, and to amplify vector DNA for Corynebacteriumtransformation. Correctly assembled vectors were confirmed byrestriction enzyme digest and Sanger sequencing. Positive clones werestored at −20° C. for future use.

C. Transformation of Assembled Clones into Corynebacterium

Verified vector clones were individually transformed intoCorynebacterium glutamicum host cells via electroporation. Each vectorwas designed to integrate into a neutral integration site within theCorynebacterium glutamicum genome that was empirically determined topermit expression of heterologous yellow fluorescence protein but not bedetrimental to the host cell. To facilitate integration, the expressionvector further comprised about 2 kbp of sequence homologous (i.e.,homology arm) to the desired integration site whereby each gene cassettedescribed above was inserted downstream of the homology am. Integrationinto the genome occurred by single-crossover integration. TransformedCorynebacterium were then tested for correct integration via PCR. Thisprocess was repeated for each of the transformations conducted for eachgene construct.

D. Evaluation of Individual Terminator Constructs in Corynebacterium

The phenotype of each Corynebacterium transformant containingpromoter-YFP-terminator constructs was then tested in two media types(brain heart infusion-BHI and HTP test media) at two time points inorder to evaluate expression. Briefly, between four and sixPCR-confirmed transformants were chosen and cultivated in selectivemedia in a 96-well format. The initial cultures were then split intoselective BHI media or selective seed media. At 48 hours, cultures inseed media were inoculated into selective HTP test media or BHI mediaand analyzed at two time points representing different portions of thegrowth curve. Time points for HTP test media cultures were 48 and 96hours after inoculation. Cultures in the selective BHI media wereanalyzed at 48 and 72 hours after inoculation.

Analysis of the cultures was performed using a benchtop flow cytometer.Briefly, cultures were diluted 1:100 in 200 μl of phosphate bufferedsaline (PBS). For each culture, between 3000 and 5000 individual events(i.e., cells) were analyzed for yellow fluorescence. The benchtop flowcytometer plots a histogram of yellow fluorescence of each “event” andcalculates the median fluorescence within each well. FIG. 36 depicts themean of the median fluorescence for each construct (across the 4-6biological replicates). Error bars indicate the 95% confidence intervalof each data point. Conditions A-D each refer to a single media and asingle time point. Thus conditions A and B represent the two time pointsfor the BHI media, while the C and D points represent the two timepoints for the HTP test media. Note that the arbitrary units (e.g., AU)represent the median fluorescence recorded by the benchtop flowcytometer.

The results show that terminators 1-8 of the STOP swap genetic designlibrary result in a continuous range of YFP expression. Theseterminators thus form a terminator ladder that can be implemented intofuture genetic design libraries, according to the HTP methods of thepresent disclosure.

Example 9: Comparing HTP Toolsets vs. Traditional UV Mutations

This example demonstrates the benefits of the HTP genetic designlibraries of the present disclosure over traditional mutational strainimprovement programs. The experiments in this portion of thespecification quantify the improved magnitude and speed of thephenotypical improvements achieved through the HTP methods of thepresent disclosure over traditional UV mutagenesis.

The present disclosure teaches new methods for accelerating the strainimprovement programs of host cells. In some embodiments, the HTP strainimprovement program of the present disclosure relies on the ability ofthe HTP toolsets to generate and identify genetic perturbations. Thepresent inventors attempted to quantify the benefits of the HTP toolsets by conducting a small parallel track strain improvement programcomparing the promoter swap techniques of the present disclosure againsttraditional UV mutations approaches.

A base reference strain producing a biochemical metabolite of interestwas chosen as the starting point for both UV and promoter swap geneticperturbations.

A. UV Mutations

Cultures of the base strain were grown in BHI medium in cultures thatwere OD normalized to OD₆₀₀ of 10. This culture was aliquoted into asterile petri dish and agitated using a small magnetic stirrer bar. A UVtrans illuminator at 254 nm wavelength was then inverted over theculture and aliquots taken at 5 and 9 minutes of UV exposure. Thesesamples were serially diluted 10-fold and each dilution plated onto BHImedium Q-trays. From these Q-trays, approximately 2500 colonies fromeach UV exposure point were picked using an automated colony pickingapparatus and the performance evaluated as below.

B. Promoter Swap

PRO swap constructs were generated in the base strain for 15 genetargets using either all or a subset of promoters selected from P1, P3,P4 and P8 described in Table 1. The final step in the biosynthesis ofthe product of interest is catalyzed by an O-methyltransferase enzymethat utilizes the potentially rate limiting cofactorS-adenosylmethionine. Gene targets for PRO swaps were therefore selectedon the basis that they are directly involved in the biosynthesis of thiscofactor or upstream metabolites.

C. UV and Promoter Swap Library Evaluation

The phenotype of each Corynebacterium strain developed for this examplewas tested for its ability to produce a selected biomolecule. Briefly,between four and six sequence confirmed colonies from each PRO swapstrain, and single colonies for each UV strain were chosen andpropagated in selective media in a 96-well format in production liquidmedia.

After biomass propagation in 96-well microwell plates, cell mass wasadded to fermentation media containing substrate in 96-well microwellplates and bioconversion was allowed to proceed for 24 hrs. Titers ofproduct were determined for each strain using high-performance liquidchromatography from samples taken at 24 hrs. The titer results for eachgenetic perturbation (UV and PRO swap) was analyzed. Results for eachreplicate was averaged and assigned to represent the overall performanceof said strain. Strains were then binned into categories based on eachmutation's effect on measured yield expressed as a ratio over the yieldof the base strain.

FIG. 37 summarizes the results of this experiment, which are presentedas the number of strains for each strain improvement technique thatproduced: i) no change in yield, ii) a 1.2 to 1.4 fold improvement toyield, iii) a 1.4 to 1.6 fold improvement to yield, iv) a 1.6 to 1.8fold improvement to yield, or v) a 1.8 to 2 fold improvement to yield.

The results are illustrative of the benefits of the HTP toolsets of thepresent disclosure over traditional UV mutagenesis approaches. Forexample, the results of FIG. 37 demonstrate that the PRO swap strainsexhibited a higher rate of positive changes in yield, and were thereforemore likely to provide mutations that could significantly improve thestrain. Most striking, was the high incidence of high improvementstrains showing 1.6, 1.8 and 2 fold increases in the PRO swap library,with little to no identified improvements in the UV library.

The results are also important because they highlight the acceleratedrate of improvement of the PRO swap methods of the present disclosure.Indeed, results for the PRO swap library were based on less than 100promoter::gene perturbations, whereas UV mutation results included thescreening of over 4,000 distinct mutant strains. Thus the methods of thepresent disclosure drastically reduce the number of mutants that must bescreened before identifying genetic perturbations capable of conferringstrains with high gains in performance.

Example 10: Application of HTP Engineering Methods in Eukaryotes

Previous examples illustrate applications of HTP strain improvementprograms on prokaryotic cells. This example demonstrates theapplicability of the same techniques to eukaryotic cells. Specifically,Examples 10 and 11 describe a SNP swap strain improvement program forAspergillus niger for the industrial production of citric acid.

A. Aspergillus Niger Protoplast Formation and Transformation

A large volume (500 ml) of protoplasts of a eukaryotic fungal strain ofAspergillus niger, ATCC 1015, was generated using a commerciallyavailable enzyme mixture which contains beta-glucanase activity. Theprotoplasts were isolated from the enzyme mixture by centrifugation andwere ultimately re-suspended in a buffer containing calcium chloride.

The protoplasts were aliquoted and frozen at negative 80 degrees Celsiusin containers containing a suspension of dimethyl sulfoxide andpolyethylene glycol (PEG). In some embodiments, the present disclosureteaches that a stock of 96-well microtiter plates containing 25-50microliters of protoplasts in each well can be prepared and frozen inlarge batches for large scale genome editing campaigns using thistechnique.

Traditional PEG Calcium mediated transformations were carried out byautomated liquid handlers, which combined the DNA with theprotoplast-PEG mixtures in the 96 wells. An additional automated liquidhandling step was used to plate the transformation on to selective mediaafter transformation.

B. Automated Screening of Transformants

As discussed in more detail below, the A. niger cells had beentransformed with a functional pyrG gene, which permitted transformedcells to grow in the absence of Uracil. The pyrG gene of this examplewas further designed to incorporate into the location of A. niger's wildtype aygA gene, thus incorporating a mutation into to the naturallyoccurring aygA gene. Disruption the aygA gene further results in ayellow spore color, providing a secondary screening method foridentifying transformants.

Transformants grown on the selective media without Uracil were isolatedand placed into individual wells of a second microtiter plate. Thetransformants in the second microtiter plate were allowed to grow andsporulate for 2-3 days, before being resuspended in a liquid consistingof water and a small amount of detergent to generate a spore stocksuitable for storage and downstream automated screening.

A small aliquot of each of the aforementioned spore stocks was then usedto inoculate liquid media in a third 96 well PCR plate. These smallcultures are allowed to grow over night in a stationary incubator sothat the yellow-pigment containing spores germinate and form hyphae thatare more amenable to selection, and downstream steps.

Following the culturing step, the hyphae of the third PCR plate werelysed by adding a commercially available buffer and heating the culturesto 99 degrees Celsius for 20 minutes. The plates were then centrifugedto separate the DNA suspension supernatant from the cell/organellepellets. The DNA extractions were then used for PCR analysis to identifycell lines comprising the desired DNA modifications.

C. Co-Transformation for Integration of SNPs-Design of SNPs

The DNA sequence of the Aspergillus niger gene aygA was obtained and theproper reading frame was determined. Four distinct types of mutationswere designed, which if integrated would result in a null mutation.

The mutations included a single base pair change that incorporates anin-frame stop codon, a small two base pair deletion, a three-base pairintegration, and a larger 100 base pair deletion all of which ifproperly integrated will eliminate aygA activity. Strains lacking aygAactivity have a yellow spore phenotype. The designs were generated as insilico constructs that predicted a set of oligomers that were used tobuild the constructs using Gibson assembly.

D. Integration of SNPs by Co-Transformation

Using the transformation approach described above, amplicons containingthe small changes were incorporated into the genome of an Aspergillusniger strain 1015. As previously discussed, this strain of Aspergillusniger comprised a non functional pyrG gene, and was therefore unable togrow in the absence of exogenous uracil. Cells that had successfullyintegrated the pyrG gene were now capable of growth in the absence ofuracil. Of these pyrG+transformants, isolates that also integrated thesmall mutations in the aygA gene exhibited the yellow spore phenotype.(FIG. 43A). The presence of the mutation is also detected throughSequencing of small amplicons that contain the region targeted for theSNP exchange (FIG. 43B).

Example 11: HTP Genomic Engineering—Implementation of an HTP SNP LibraryStrain Improvement Program to Improve Citric Acid Production inEukaryote Aspergillus niger ATCC11414

Example 10 above described the techniques for automating the geneticengineering techniques of the present disclosure in a high throughputmanner. This example applies the techniques described above to thespecific HTP strain improvement of Aspergillus niger strain ATCC11414.

Aspergillus niger is a species of filamentous fungi used for the largescale production of citric acid through fermentation. Multiple strainsof this species have been isolated and shown to have varying capacityfor production of citric and other organic acids. The HTP strainengineering methods of the present disclosure can be used to combinecausative alleles and eliminate detrimental alleles to improve citricacid production.

A. Identification of a Library of Genetic Design Library for SNPs fromNatural A. niger Strain Variants.

A. niger strain ATCC 1015 was identified as a producer of citric acid inthe early twentieth century. An isolate of this strain named ATCC 11414,was later found to exhibit increased citric acid yield over its parent.For example, A. niger strain ATCC 1015 on average produces 7 grams ofcitric acid from 140 grams of glucose in media containing ammoniumnitrate, but lacking both iron and manganese cations. Isolate strainATCC 11414 on the other hand, exhibits a 10-fold yield increase (70grams of citric acid) under the same conditions. Moreover, strain ATCC11414 spores germinate and grow better in citric acid production mediathan do spores of strain 1015.

In order to identify potential genetic sources for these phenotypicdifferences, the genomes of both the ATCC 1015 and ATCC 11414 strainswere sequenced and analyzed. The resulting analysis identified 42 SNPsdistinguishing the 1015 and 11414 strains.

B. Exchanging Causative Alleles

Protoplasts were prepared from strain ATCC 1015 (“base strain”) fortransformation. Each of the above-identified 42 SNPs were thenindividually introduced into the base strain via the gene editingtechniques of the present disclosure (“wave up” FIG. 44A). Each SNP wasco-transformed with the functional pyrG and aygA gene mutation asdescribed above. Transformants that had successful gene targeting to theaygA locus produced yellow spores (FIG. 44B).

C. Screening for Successful Integration

Transformants containing putative SNPs were isolated and a spore stockwas propagated as stated above. Amplicons that contain the region of DNAcontaining the putative SNP were analyzed by next generation sequencing.Using this approach it is possible to determine successful integrationevents within each transformant even in the presence of the parentalDNA. This capability is essential to determine targeting in fungi whichcan grow as heterokaryons which contain nuclei with differing genotypein the same cell.

Transformants were further validated for presence of the desired SNPchange. The co-transformants that had the yellow spore phenotype alsocontained proper integration of the citric acid SNP in approximately 30%of the isolates (FIGS. 45 and 46).

The inventors expect to phenotypically screen the created SNP swapmicrobial strain library, in order to identify SNPs beneficial to theproduction of citric acid. The inventors will utilize this information,in the context of the HTP methods of genomic engineering describedherein, to derive an A. niger strain with increased citric acidproduction.

FURTHER EMBODIMENTS OF THE INVENTION

Other subject matter contemplated by the present disclosure is set outin the following numbered embodiments:

-   -   1. A high-throughput (HTP) method of genomic engineering to        evolve a microbe to acquire a desired phenotype, comprising:        -   a. perturbing the genomes of an initial plurality of            microbes having the same microbial strain background, to            thereby create an initial HTP genetic design microbial            strain library comprising individual microbial strains with            unique genetic variations;        -   b. screening and selecting individual microbial strains of            the initial HTP genetic design microbial strain library for            the desired phenotype;        -   c. providing a subsequent plurality of microbes that each            comprise a unique combination of genetic variation, said            genetic variation selected from the genetic variation            present in at least two individual microbial strains            screened in the preceding step, to thereby create a            subsequent HTP genetic design microbial strain library;        -   d. screening and selecting individual microbial strains of            the subsequent HTP genetic design microbial strain library            for the desired phenotype; and        -   e. repeating steps c)-d) one or more times, in a linear or            non-linear fashion, until a microbe has acquired the desired            phenotype, wherein each subsequent iteration creates a new            HTP genetic design microbial strain library comprising            individual microbial strains harboring unique genetic            variations that are a combination of genetic variation            selected from amongst at least two individual microbial            strains of a preceding HTP genetic design microbial strain            library.    -   2. The HTP method of genomic engineering according to embodiment        1, wherein the initial HTP genetic design microbial strain        library comprises at least one selected from the group        consisting of a promoter swap microbial strain library, SNP swap        microbial strain library, start/stop codon microbial strain        library, optimized sequence microbial strain library, a        terminator swap microbial strain library, and any combination        thereof    -   3. The HTP method of genomic engineering according to any one of        embodiments 1-2, wherein the subsequent HTP genetic design        microbial strain library is a full combinatorial microbial        strain library of the initial HTP genetic design microbial        strain library.    -   4. The HTP method of genomic engineering according to any one of        embodiments 1-2, wherein the subsequent HTP genetic design        microbial strain library is a subset of a full combinatorial        microbial strain library of the initial HTP genetic design        microbial strain library.    -   5. The HTP method of genomic engineering according to any one of        embodiments 1-2, wherein the subsequent HTP genetic design        microbial strain library is a full combinatorial microbial        strain library of a preceding HTP genetic design microbial        strain library.    -   6. The HTP method of genomic engineering according to any one of        embodiments 1-5, wherein the subsequent HTP genetic design        microbial strain library is a subset of a full combinatorial        microbial strain library of a preceding HTP genetic design        microbial strain library.    -   7. The HTP method of genomic engineering according to any one of        embodiments 1-5, wherein perturbing the genome comprises        utilizing at least one method selected from the group consisting        of: random mutagenesis, targeted sequence insertions, targeted        sequence deletions, targeted sequence replacements, and any        combination thereof.    -   8. The HTP method of genomic engineering according to any one of        embodiments 1-6, wherein the initial plurality of microbes        comprises unique genetic variations derived from an industrial        production strain microbe.    -   9. The HTP method of genomic engineering according to any one of        embodiments 1-6, wherein the initial plurality of microbes        comprises industrial production strain microbes denoted S₁Gen₁        and any number of subsequent microbial generations derived        therefrom denoted S_(n)Gen_(n).    -   10. A method for generating a SNP swap microbial strain library,        comprising the steps of:        -   a. providing a reference microbial strain and a second            microbial strain, wherein the second microbial strain            comprises a plurality of identified genetic variations            selected from single nucleotide polymorphisms, DNA            insertions, and DNA deletions, which are not present in the            reference microbial strain; and        -   b. perturbing the genome of either the reference microbial            strain, or the second microbial strain, to thereby create an            initial SNP swap microbial strain library comprising a            plurality of individual microbial strains with unique            genetic variations found within each strain of said            plurality of individual microbial strains, wherein each of            said unique genetic variations corresponds to a single            genetic variation selected from the plurality of identified            genetic variations between the reference microbial strain            and the second microbial strain.    -   11. The method for generating a SNP swap microbial strain        library according to embodiment 10, wherein the genome of the        reference microbial strain is perturbed to add one or more of        the identified single nucleotide polymorphisms, DNA insertions,        or DNA deletions, which are found in the second microbial        strain.    -   12. The method for generating a SNP swap microbial strain        library according to embodiment 10, wherein the genome of the        second microbial strain is perturbed to remove one or more of        the identified single nucleotide polymorphisms, DNA insertions,        or DNA deletions, which are not found in the reference microbial        strain.    -   13. The method for generating a SNP swap microbial strain        library according to any one of embodiments 10-12, wherein the        resultant plurality of individual microbial strains with unique        genetic variations, together comprise a full combinatorial        library of all the identified genetic variations between the        reference microbial strain and the second microbial strain.    -   14. The method for generating a SNP swap microbial strain        library according to any one of embodiments 10-12, wherein the        resultant plurality of individual microbial strains with unique        genetic variations, together comprise a subset of a full        combinatorial library of all the identified genetic variations        between the reference microbial strain and the second microbial        strain.    -   15. A method for rehabilitating and improving the phenotypic        performance of an industrial microbial strain, comprising the        steps of:        -   a. providing a parental lineage microbial strain and an            industrial microbial strain derived therefrom, wherein the            industrial microbial strain comprises a plurality of            identified genetic variations selected from single            nucleotide polymorphisms, DNA insertions, and DNA deletions,            not present in the parental lineage microbial strain;        -   b. perturbing the genome of either the parental lineage            microbial strain, or the industrial microbial strain, to            thereby create an initial SNP swap microbial strain library            comprising a plurality of individual microbial strains with            unique genetic variations found within each strain of said            plurality of individual microbial strains, wherein each of            said unique genetic variations corresponds to a single            genetic variation selected from the plurality of identified            genetic variations between the parental lineage microbial            strain and the industrial microbial strain;        -   c. screening and selecting individual microbial strains of            the initial SNP swap microbial strain library for phenotype            performance improvements over a reference microbial strain,            thereby identifying unique genetic variations that confer            said individual microbial strains with phenotype performance            improvements;        -   d. providing a subsequent plurality of microbes that each            comprise a unique combination of genetic variation, said            genetic variation selected from the genetic variation            present in at least two individual microbial strains            screened in the preceding step, to thereby create a            subsequent SNP swap microbial strain library;        -   e. screening and selecting individual microbial strains of            the subsequent SNP swap microbial strain library for            phenotype performance improvements over the reference            microbial strain, thereby identifying unique combinations of            genetic variation that confer said microbial strains with            additional phenotype performance improvements; and        -   f. repeating steps d)-e) one or more times, in a linear or            non-linear fashion, until a microbial strain exhibits a            desired level of improved phenotype performance compared to            the phenotype performance of the industrial microbial            strain, wherein each subsequent iteration creates a new SNP            swap microbial strain library comprising individual            microbial strains harboring unique genetic variations that            are a combination of genetic variation selected from amongst            at least two individual microbial strains of a preceding SNP            swap microbial strain library.    -   15.1. The method for rehabilitating and improving the phenotypic        performance of an industrial microbial strain according to        embodiment 15, wherein the identified genetic variations further        comprise artificial promoter swap genetic variations from a        promoter swap library.    -   16. The method for rehabilitating and improving the phenotypic        performance of an industrial microbial strain according to any        one of embodiments 15-15.1, wherein the resultant plurality of        individual microbial strains with unique genetic variations,        together comprise a full combinatorial library of all the        identified genetic variations between the reference microbial        strain and the second microbial strain.    -   17. The method for rehabilitating and improving the phenotypic        performance of an industrial microbial strain according to any        one of embodiments 15-15.1, wherein the resultant plurality of        individual microbial strains with unique genetic variations,        together comprise a subset of a full combinatorial library of        all the identified genetic variations between the reference        microbial strain and the second microbial strain.    -   18. The method for rehabilitating and improving the phenotypic        performance of an industrial microbial strain according to any        one of embodiments 15-17, wherein the resultant subsequent        plurality of individual microbial strains with unique        combinations of genetic variations, together comprise a subset        of a full combinatorial library of all the genetic variations        present in the individual microbial strains screened in the        preceding step.    -   19. The method for rehabilitating and improving the phenotypic        performance of an industrial microbial strain according to any        one of embodiments 15-18, wherein the genome of the parental        lineage microbial strain is perturbed to add one or more of the        identified single nucleotide polymorphisms, DNA insertions, or        DNA deletions, which are found in the industrial microbial        strain.    -   20. The method for rehabilitating and improving the phenotypic        performance of an industrial microbial strain according to any        one of embodiments 15-18, wherein the genome of the industrial        microbial strain is perturbed to remove one or more of the        identified single nucleotide polymorphisms, DNA insertions, or        DNA deletions, which are not found in the parental lineage        microbial strain.    -   21. A method for generating a promoter swap microbial strain        library, said method comprising the steps of:        -   a. providing a plurality of target genes endogenous to a            base microbial strain, and a promoter ladder, wherein said            promoter ladder comprises a plurality of promoters            exhibiting different expression profiles in the base            microbial strain; and        -   b. engineering the genome of the base microbial strain, to            thereby create an initial promoter swap microbial strain            library comprising a plurality of individual microbial            strains with unique genetic variations found within each            strain of said plurality of individual microbial strains,            wherein each of said unique genetic variations comprises one            or more of the promoters from the promoter ladder operably            linked to one of the target genes endogenous to the base            microbial strain.    -   22. A promoter swap method of genomic engineering to evolve a        microbe to acquire a desired phenotype, said method comprising        the steps of:        -   a. providing a plurality of target genes endogenous to a            base microbial strain, and a promoter ladder, wherein said            promoter ladder comprises a plurality of promoters            exhibiting different expression profiles in the base            microbial strain;        -   b. engineering the genome of the base microbial strain, to            thereby create an initial promoter swap microbial strain            library comprising a plurality of individual microbial            strains with unique genetic variations found within each            strain of said plurality of individual microbial strains,            wherein each of said unique genetic variations comprises one            or more of the promoters from the promoter ladder operably            linked to one of the target genes endogenous to the base            microbial strain;        -   c. screening and selecting individual microbial strains of            the initial promoter swap microbial strain library for the            desired phenotype;        -   d. providing a subsequent plurality of microbes that each            comprise a unique combination of genetic variation, said            genetic variation selected from the genetic variation            present in at least two individual microbial strains            screened in the preceding step, to thereby create a            subsequent promoter swap microbial strain library;        -   e. screening and selecting individual microbial strains of            the subsequent promoter swap microbial strain library for            the desired phenotype; and        -   f. repeating steps d)-e) one or more times, in a linear or            non-linear fashion, until a microbe has acquired the desired            phenotype, wherein each subsequent iteration creates a new            promoter swap microbial strain library comprising individual            microbial strains harboring unique genetic variations that            are a combination of genetic variation selected from amongst            at least two individual microbial strains of a preceding            promoter swap microbial strain library.    -   23. The promoter swap method of genomic engineering to evolve a        microbe to acquire a desired phenotype according to embodiment        22, wherein the resultant subsequent plurality of individual        microbial strains with unique combinations of genetic        variations, together comprise a subset of a full combinatorial        library of all the genetic variations present in the individual        microbial strains screened in the preceding step.    -   23.1. The promoter swap method of genomic engineering to evolve        a microbe to acquire a desired phenotype according to embodiment        22, wherein the resultant subsequent plurality of individual        microbial strains with unique combinations of genetic        variations, together comprise a full combinatorial library of        all the genetic variations present in the individual microbial        strains screened in the preceding step.    -   24. A method for generating a terminator swap microbial strain        library, said method comprising the steps of:        -   a. providing a plurality of target genes endogenous to a            base microbial strain, and a terminator ladder, wherein said            terminator ladder comprises a plurality of terminators            exhibiting different expression profiles in the base            microbial strain; and        -   b. engineering the genome of the base microbial strain, to            thereby create an initial terminator swap microbial strain            library comprising a plurality of individual microbial            strains with unique genetic variations found within each            strain of said plurality of individual microbial strains,            wherein each of said unique genetic variations comprises one            of the target genes endogenous to the base microbial strain            operably linked to one or more of the terminators from the            terminator ladder.    -   25. A terminator swap method of genomic engineering to evolve a        microbe to acquire a desired phenotype, said method comprising        the steps of:        -   a. providing a plurality of target genes endogenous to a            base microbial strain, and a terminator ladder, wherein said            terminator ladder comprises a plurality of terminators            exhibiting different expression profiles in the base            microbial strain;        -   b. engineering the genome of the base microbial strain, to            thereby create an initial terminator swap microbial strain            library comprising a plurality of individual microbial            strains with unique genetic variations found within each            strain of said plurality of individual microbial strains,            wherein each of said unique genetic variations comprises one            of the target genes endogenous to the base microbial strain            operably linked to one or more of the terminators from the            terminator ladder;        -   c. screening and selecting individual microbial strains of            the initial terminator swap microbial strain library for the            desired phenotype;        -   d. providing a subsequent plurality of microbes that each            comprise a unique combination of genetic variation, said            genetic variation selected from the genetic variation            present in at least two individual microbial strains            screened in the preceding step, to thereby create a            subsequent terminator swap microbial strain library;        -   e. screening and selecting individual microbial strains of            the subsequent terminator swap microbial strain library for            the desired phenotype; and        -   f. repeating steps d)-e) one or more times, in a linear or            non-linear fashion, until a microbe has acquired the desired            phenotype, wherein each subsequent iteration creates a new            terminator swap microbial strain library comprising            individual microbial strains harboring unique genetic            variations that are a combination of genetic variation            selected from amongst at least two individual microbial            strains of a preceding terminator swap microbial strain            library.    -   26. The terminator swap method of genomic engineering to evolve        a microbe to acquire a desired phenotype according to embodiment        25, wherein the resultant subsequent plurality of individual        microbial strains with unique combinations of genetic        variations, together comprise a subset of a full combinatorial        library of all the genetic variations present in the individual        microbial strains screened in the preceding step.    -   26.1. The terminator swap method of genomic engineering to        evolve a microbe to acquire a desired phenotype according to        embodiment 25, wherein the resultant subsequent plurality of        individual microbial strains with unique combinations of genetic        variations, together comprise a full combinatorial library of        all the genetic variations present in the individual microbial        strains screened in the preceding step.    -   27. A high-throughput (HTP) genomic engineering system for        evolving a microbe to acquire a desired phenotype, the system        comprising:        -   one or more processors; and        -   one or more memories operatively coupled to at least one of            the one or more processors and having instructions stored            thereon that, when executed by at least one of the one or            more processors, cause the system to:        -   a. perturb the genomes of an initial plurality of microbes            having the same microbial strain background, to thereby            create an initial HTP genetic design microbial strain            library comprising individual microbial strains with unique            genetic variations;        -   b. screen and select individual microbial strains of the            initial HTP genetic design microbial strain library for the            desired phenotype;        -   c. provide a subsequent plurality of microbes that each            comprise a unique combination of genetic variation, said            genetic variation selected from the genetic variation            present in at least two individual microbial strains            screened in the preceding step, to thereby create a            subsequent HTP genetic design microbial strain library;        -   d. screen and select individual microbial strains of the            subsequent HTP genetic design microbial strain library for            the desired phenotype; and        -   e. repeat steps c)-d) one or more times, in a linear or            non-linear fashion, until a microbe has acquired the desired            phenotype, wherein each subsequent iteration creates a new            HTP genetic design microbial strain library comprising            individual microbial strains harboring unique genetic            variations that are a combination of genetic variation            selected from amongst at least two individual microbial            strains of a preceding HTP genetic design microbial strain            library.    -   28. One or more non-transitory computer readable media storing        instructions for evolving a microbe to acquire a desired        phenotype, wherein the instructions, when executed by one or        more computing devices, cause at least one of the one or more        computing devices to:        -   a. perturb the genomes of an initial plurality of microbes            having the same microbial strain background, to thereby            create an initial HTP genetic design microbial strain            library comprising individual microbial strains with unique            genetic variations;        -   b. screen and select individual microbial strains of the            initial HTP genetic design microbial strain library for the            desired phenotype;        -   c. provide a subsequent plurality of microbes that each            comprise a unique combination of genetic variation, said            genetic variation selected from the genetic variation            present in at least two individual microbial strains            screened in the preceding step, to thereby create a            subsequent HTP genetic design microbial strain library;        -   d. screen and select individual microbial strains of the            subsequent HTP genetic design microbial strain library for            the desired phenotype; and        -   e. repeat steps c)-d) one or more times, in a linear or            non-linear fashion, until a microbe has acquired the desired            phenotype, wherein each subsequent iteration creates a new            HTP genetic design microbial strain library comprising            individual microbial strains harboring unique genetic            variations that are a combination of genetic variation            selected from amongst at least two individual microbial            strains of a preceding HTP genetic design microbial strain            library.    -   29. A system for generating a SNP swap microbial strain library,        the system comprising:        -   one or more processors; and        -   one or more memories operatively coupled to at least one of            the one or more processors and having instructions stored            thereon that, when executed by at least one of the one or            more processors, cause the system to:        -   a. provide a reference microbial strain and a second            microbial strain, wherein the second microbial strain            comprises a plurality of identified genetic variations            selected from single nucleotide polymorphisms, DNA            insertions, and DNA deletions, which are not present in the            reference microbial strain; and        -   b. perturb the genome of either the reference microbial            strain, or the second microbial strain, to thereby create an            initial SNP swap microbial strain library comprising a            plurality of individual microbial strains with unique            genetic variations found within each strain of said            plurality of individual microbial strains, wherein each of            said unique genetic variations corresponds to a single            genetic variation selected from the plurality of identified            genetic variations between the reference microbial strain            and the second microbial strain.    -   30. One or more non-transitory computer readable media storing        instructions for generating a SNP swap microbial strain library,        wherein the instructions, when executed by one or more computing        devices, cause at least one of the one or more computing devices        to:        -   a. provide a reference microbial strain and a second            microbial strain, wherein the second microbial strain            comprises a plurality of identified genetic variations            selected from single nucleotide polymorphisms, DNA            insertions, and DNA deletions, which are not present in the            reference microbial strain; and        -   b. perturb the genome of either the reference microbial            strain, or the second microbial strain, to thereby create an            initial SNP swap microbial strain library comprising a            plurality of individual microbial strains with unique            genetic variations found within each strain of said            plurality of individual microbial strains, wherein each of            said unique genetic variations corresponds to a single            genetic variation selected from the plurality of identified            genetic variations between the reference microbial strain            and the second microbial strain.    -   31. A system for rehabilitating and improving the phenotypic        performance of an industrial microbial strain, the system        comprising:        -   one or more processors; and        -   one or more memories operatively coupled to at least one of            the one or more processors and having instructions stored            thereon that, when executed by at least one of the one or            more processors, cause the system to:        -   a. provide a parental lineage microbial strain and an            industrial microbial strain derived therefrom, wherein the            industrial microbial strain comprises a plurality of            identified genetic variations selected from single            nucleotide polymorphisms, DNA insertions, and DNA deletions,            not present in the parental lineage microbial strain;        -   b. perturb the genome of either the parental lineage            microbial strain, or the industrial microbial strain, to            thereby create an initial SNP swap microbial strain library            comprising a plurality of individual microbial strains with            unique genetic variations found within each strain of said            plurality of individual microbial strains, wherein each of            said unique genetic variations corresponds to a single            genetic variation selected from the plurality of identified            genetic variations between the parental lineage microbial            strain and the industrial microbial strain;        -   c. screen and select individual microbial strains of the            initial SNP swap microbial strain library for phenotype            performance improvements over a reference microbial strain,            thereby identifying unique genetic variations that confer            said microbial strains with phenotype performance            improvements;        -   d. provide a subsequent plurality of microbes that each            comprise a unique combination of genetic variation, said            genetic variation selected from the genetic variation            present in at least two individual microbial strains            screened in the preceding step, to thereby create a            subsequent SNP swap microbial strain library;        -   e. screen and select individual microbial strains of the            subsequent SNP swap microbial strain library for phenotype            performance improvements over the reference microbial            strain, thereby identifying unique combinations of genetic            variation that confer said microbial strains with additional            phenotype performance improvements; and        -   f. repeat steps d)-e) one or more times, in a linear or            non-linear fashion, until a microbial strain exhibits a            desired level of improved phenotype performance compared to            the phenotype performance of the industrial microbial            strain, wherein each subsequent iteration creates a new SNP            swap microbial strain library comprising individual            microbial strains harboring unique genetic variations that            are a combination of genetic variation selected from amongst            at least two individual microbial strains of a preceding SNP            swap microbial strain library.    -   32. One or more non-transitory computer readable media storing        instructions for rehabilitating and improving the phenotypic        performance of an industrial microbial strain, wherein the        instructions, when executed by one or more computing devices,        cause at least one of the one or more computing devices to:        -   a. provide a parental lineage microbial strain and an            industrial microbial strain derived therefrom, wherein the            industrial microbial strain comprises a plurality of            identified genetic variations selected from single            nucleotide polymorphisms, DNA insertions, and DNA deletions,            not present in the parental lineage microbial strain;        -   b. perturb the genome of either the parental lineage            microbial strain, or the industrial microbial strain, to            thereby create an initial SNP swap microbial strain library            comprising a plurality of individual microbial strains with            unique genetic variations found within each strain of said            plurality of individual microbial strains, wherein each of            said unique genetic variations corresponds to a single            genetic variation selected from the plurality of identified            genetic variations between the parental lineage microbial            strain and the industrial microbial strain;        -   c. screen and select individual microbial strains of the            initial SNP swap microbial strain library for phenotype            performance improvements over a reference microbial strain,            thereby identifying unique genetic variations that confer            said microbial strains with phenotype performance            improvements;        -   d. provide a subsequent plurality of microbes that each            comprise a unique combination of genetic variation, said            genetic variation selected from the genetic variation            present in at least two individual microbial strains            screened in the preceding step, to thereby create a            subsequent SNP swap microbial strain library;        -   e. screen and select individual microbial strains of the            subsequent SNP swap microbial strain library for phenotype            performance improvements over the reference microbial            strain, thereby identifying unique combinations of genetic            variation that confer said microbial strains with additional            phenotype performance improvements; and        -   f. repeat steps d)-e) one or more times, in a linear or            non-linear fashion, until a microbial strain exhibits a            desired level of improved phenotype performance compared to            the phenotype performance of the industrial microbial            strain, wherein each subsequent iteration creates a new SNP            swap microbial strain library comprising individual            microbial strains harboring unique genetic variations that            are a combination of genetic variation selected from amongst            at least two individual microbial strains of a preceding SNP            swap microbial strain library.    -   33. A system for generating a promoter swap microbial strain        library, the system comprising:        -   one or more processors; and        -   one or more memories operatively coupled to at least one of            the one or more processors and having instructions stored            thereon that, when executed by at least one of the one or            more processors, cause the system to:        -   a. provide a plurality of target genes endogenous to a base            microbial strain, and a promoter ladder, wherein said            promoter ladder comprises a plurality of promoters            exhibiting different expression profiles in the base            microbial strain; and        -   b. engineer the genome of the base microbial strain, to            thereby create an initial promoter swap microbial strain            library comprising a plurality of individual microbial            strains with unique genetic variations found within each            strain of said plurality of individual microbial strains,            wherein each of said unique genetic variations comprises one            or more of the promoters from the promoter ladder operably            linked to one of the target genes endogenous to the base            microbial strain.    -   34. One or more non-transitory computer readable media storing        instructions for generating a promoter swap microbial strain        library, wherein the instructions, when executed by one or more        computing devices, cause at least one of the one or more        computing devices to:        -   a. provide a plurality of target genes endogenous to a base            microbial strain, and a promoter ladder, wherein said            promoter ladder comprises a plurality of promoters            exhibiting different expression profiles in the base            microbial strain; and        -   b. engineer the genome of the base microbial strain, to            thereby create an initial promoter swap microbial strain            library comprising a plurality of individual microbial            strains with unique genetic variations found within each            strain of said plurality of individual microbial strains,            wherein each of said unique genetic variations comprises one            or more of the promoters from the promoter ladder operably            linked to one of the target genes endogenous to the base            microbial strain.    -   35. A genomic engineering system to evolve a microbe through        promoter swapping to acquire a desired phenotype, the system        comprising:        -   one or more processors; and        -   one or more memories operatively coupled to at least one of            the one or more processors and having instructions stored            thereon that, when executed by at least one of the one or            more processors, cause the system to:        -   a. provide a plurality of target genes endogenous to a base            microbial strain, and a promoter ladder, wherein said            promoter ladder comprises a plurality of promoters            exhibiting different expression profiles in the base            microbial strain;        -   b. engineer the genome of the base microbial strain, to            thereby create an initial promoter swap microbial strain            library comprising a plurality of individual microbial            strains with unique genetic variations found within each            strain of said plurality of individual microbial strains,            wherein each of said unique genetic variations comprises one            or more of the promoters from the promoter ladder operably            linked to one of the target genes endogenous to the base            microbial strain;        -   c. screen and select individual microbial strains of the            initial promoter swap microbial strain library for the            desired phenotype;        -   d. provide a subsequent plurality of microbes that each            comprise a unique combination of genetic variation, said            genetic variation selected from the genetic variation            present in at least two individual microbial strains            screened in the preceding step, to thereby create a            subsequent promoter swap microbial strain library;        -   e. screen and select individual microbial strains of the            subsequent promoter swap microbial strain library for the            desired phenotype; and        -   f. repeat steps d)-e) one or more times, in a linear or            non-linear fashion, until a microbe has acquired the desired            phenotype, wherein each subsequent iteration creates a new            promoter swap microbial strain library comprising individual            microbial strains harboring unique genetic variations that            are a combination of genetic variation selected from amongst            at least two individual microbial strains of a preceding            promoter swap microbial strain library.    -   36. One or more non-transitory computer readable media storing        instructions for evolving a microbe through promoter swapping to        acquire a desired phenotype, wherein the instructions, when        executed by one or more computing devices, cause at least one of        the one or more computing devices to:        -   a. provide a plurality of target genes endogenous to a base            microbial strain, and a promoter ladder, wherein said            promoter ladder comprises a plurality of promoters            exhibiting different expression profiles in the base            microbial strain;        -   b. engineer the genome of the base microbial strain, to            thereby create an initial promoter swap microbial strain            library comprising a plurality of individual microbial            strains with unique genetic variations found within each            strain of said plurality of individual microbial strains,            wherein each of said unique genetic variations comprises one            or more of the promoters from the promoter ladder operably            linked to one of the target genes endogenous to the base            microbial strain;        -   c. screen and select individual microbial strains of the            initial promoter swap microbial strain library for the            desired phenotype;        -   d. provide a subsequent plurality of microbes that each            comprise a unique combination of genetic variation, said            genetic variation selected from the genetic variation            present in at least two individual microbial strains            screened in the preceding step, to thereby create a            subsequent promoter swap microbial strain library;        -   e. screen and select individual microbial strains of the            subsequent promoter swap microbial strain library for the            desired phenotype; and        -   f. repeat steps d)-e) one or more times, in a linear or            non-linear fashion, until a microbe has acquired the desired            phenotype, wherein each subsequent iteration creates a new            promoter swap microbial strain library comprising individual            microbial strains harboring unique genetic variations that            are a combination of genetic variation selected from amongst            at least two individual microbial strains of a preceding            promoter swap microbial strain library.    -   37. A system for generating a terminator swap microbial strain        library, the system comprising:        -   one or more processors; and        -   one or more memories operatively coupled to at least one of            the one or more processors and having instructions stored            thereon that, when executed by at least one of the one or            more processors, cause the system to:        -   a. provide a plurality of target genes endogenous to a base            microbial strain, and a terminator ladder, wherein said            terminator ladder comprises a plurality of terminators            exhibiting different expression profiles in the base            microbial strain; and        -   b. engineer the genome of the base microbial strain, to            thereby create an initial terminator swap microbial strain            library comprising a plurality of individual microbial            strains with unique genetic variations found within each            strain of said plurality of individual microbial strains,            wherein each of said unique genetic variations comprises one            of the target genes endogenous to the base microbial strain            operably linked to one or more of the terminators from the            terminator ladder.    -   38. One or more non-transitory computer readable media storing        instructions for generating a terminator swap microbial strain        library, wherein the instructions, when executed by one or more        computing devices, cause at least one of the one or more        computing devices to:        -   a. provide a plurality of target genes endogenous to a base            microbial strain, and a terminator ladder, wherein said            terminator ladder comprises a plurality of terminators            exhibiting different expression profiles in the base            microbial strain; and        -   b. engineer the genome of the base microbial strain, to            thereby create an initial terminator swap microbial strain            library comprising a plurality of individual microbial            strains with unique genetic variations found within each            strain of said plurality of individual microbial strains,            wherein each of said unique genetic variations comprises one            of the target genes endogenous to the base microbial strain            operably linked to one or more of the terminators from the            terminator ladder.    -   39. A genomic engineering system to evolve through terminator        swapping a microbe to acquire a desired phenotype, the system        comprising:        -   one or more processors; and        -   one or more memories operatively coupled to at least one of            the one or more processors and having instructions stored            thereon that, when executed by at least one of the one or            more processors, cause the system to:        -   a. provide a plurality of target genes endogenous to a base            microbial strain, and a terminator ladder, wherein said            terminator ladder comprises a plurality of terminators            exhibiting different expression profiles in the base            microbial strain;        -   b. engineer the genome of the base microbial strain, to            thereby create an initial terminator swap microbial strain            library comprising a plurality of individual microbial            strains with unique genetic variations found within each            strain of said plurality of individual microbial strains,            wherein each of said unique genetic variations comprises one            of the target genes endogenous to the base microbial strain            operably linked to one or more of the terminators from the            terminator ladder;        -   c. screen and select individual microbial strains of the            initial terminator swap microbial strain library for the            desired phenotype;        -   d. provide a subsequent plurality of microbes that each            comprise a unique combination of genetic variation, said            genetic variation selected from the genetic variation            present in at least two individual microbial strains            screened in the preceding step, to thereby create a            subsequent terminator swap microbial strain library;        -   e. screen and select individual microbial strains of the            subsequent terminator swap microbial strain library for the            desired phenotype; and        -   f. repeat steps d)-e) one or more times, in a linear or            non-linear fashion, until a microbe has acquired the desired            phenotype, wherein each subsequent iteration creates a new            terminator swap microbial strain library comprising            individual microbial strains harboring unique genetic            variations that are a combination of genetic variation            selected from amongst at least two individual microbial            strains of a preceding terminator swap microbial strain            library.    -   40. One or more non-transitory computer readable media storing        instructions for evolving through terminator swapping a microbe        to acquire a desired phenotype, wherein the instructions, when        executed by one or more computing devices, cause at least one of        the one or more computing devices to:        -   a. provide a plurality of target genes endogenous to a base            microbial strain, and a terminator ladder, wherein said            terminator ladder comprises a plurality of terminators            exhibiting different expression profiles in the base            microbial strain;        -   b. engineer the genome of the base microbial strain, to            thereby create an initial terminator swap microbial strain            library comprising a plurality of individual microbial            strains with unique genetic variations found within each            strain of said plurality of individual microbial strains,            wherein each of said unique genetic variations comprises one            of the target genes endogenous to the base microbial strain            operably linked to one or more of the terminators from the            terminator ladder;        -   c. screen and select individual microbial strains of the            initial terminator swap microbial strain library for the            desired phenotype;        -   d. provide a subsequent plurality of microbes that each            comprise a unique combination of genetic variation, said            genetic variation selected from the genetic variation            present in at least two individual microbial strains            screened in the preceding step, to thereby create a            subsequent terminator swap microbial strain library;        -   e. screen and select individual microbial strains of the            subsequent terminator swap microbial strain library for the            desired phenotype; and        -   f. repeat steps d)-e) one or more times, in a linear or            non-linear fashion, until a microbe has acquired the desired            phenotype, wherein each subsequent iteration creates a new            terminator swap microbial strain library comprising            individual microbial strains harboring unique genetic            variations that are a combination of genetic variation            selected from amongst at least two individual microbial            strains of a preceding terminator swap microbial strain            library.    -   41. A computer-implemented method for iteratively improving the        design of candidate microbial strains, the method comprising:        -   a. accessing a predictive model populated with a training            set comprising (1) inputs representing genetic changes to            one or more background microbial strains and (2)            corresponding performance measures;        -   b. applying test inputs to the predictive model that            represent genetic changes, the test inputs corresponding to            candidate microbial strains incorporating those genetic            changes;        -   c. predicting phenotypic performance of the candidate            microbial strains based at least in part upon the predictive            model;        -   d. selecting a first subset of the candidate microbial            strains based at least in part upon their predicted            performance;        -   e. obtaining measured phenotypic performance of the first            subset of the candidate microbial strains;        -   f. obtaining a selection of a second subset of the candidate            microbial strains based at least in part upon their measured            phenotypic performance;        -   g. adding to the training set of the predictive model (1)            inputs corresponding to the selected second subset of            candidate microbial strains, along with (2) corresponding            measured performance of the selected second subset of            candidate microbial strains; and        -   h. repeating (b)-(g).    -   42. The method of embodiment 41, wherein repeating (b)-(g)        comprises repeating (b)-(g) until measured phenotypic        performance of at least one candidate microbial strain satisfies        a performance metric.    -   43. The method of embodiment 41, wherein:        -   during a first application of test inputs to the predictive            model, the genetic changes represented by the test inputs            comprise genetic changes to the one or more background            microbial strains; and        -   during subsequent applications of test inputs, the genetic            changes represented by the test inputs comprise genetic            changes to candidate microbial strains within a previously            selected second subset of candidate microbial strains.    -   44. The method of embodiment 41, wherein the selection of the        first subset of the candidate microbial strains is based at        least in part upon epistatic effects.    -   45. The method of embodiment 44, wherein the selection of the        first subset based at least in part upon epistatic effects        comprises:        -   during a first selection of the first subset:        -   determining degrees of dissimilarity between performance            measures of the one or more background microbial strains in            response to application of a plurality of respective inputs            representing genetic changes to the one or more background            microbial strains; and        -   selecting for inclusion in the first subset at least two            candidate microbial strains based at least in part upon the            degrees of dissimilarity in the performance measures of the            one or more background microbial strains in response to            application of genetic changes incorporated into the at            least two candidate microbial strains.    -   46. The method of embodiment 45, further comprising:        -   during subsequent selections of the first subset:        -   determining degrees of dissimilarity between performance            measures of previous first subset candidate microbial            strains in response to application of a plurality of            respective inputs representing genetic changes, wherein the            previous first subset candidate microbial strains are            strains that were selected during a previous selection of            the first subset; and        -   selecting for inclusion into the first subset at least two            candidate microbial strains based at least in part upon the            degrees of dissimilarity in the performance measures of the            previous first subset candidate microbial strains in            response to application of the genetic changes incorporated            into the at least two candidate microbial strains.    -   47. A system for iteratively improving the design of candidate        microbial strains, the system comprising:        -   one or more processors; and        -   one or more memories operatively coupled to at least one of            the one or more processors and having instructions stored            thereon that, when executed by at least one of the one or            more processors, cause the system to:            -   a. access a predictive model populated with a training                set comprising (1) inputs representing genetic changes                to one or more background microbial strains and (2)                corresponding performance measures;            -   b. apply test inputs to the predictive model that                represent genetic changes, the test inputs corresponding                to candidate microbial strains incorporating those                genetic changes;            -   c. predict phenotypic performance of the candidate                microbial strains based at least in part upon the                predictive model;            -   d. select a first subset of the candidate microbial                strains based at least in part upon their predicted                performance;            -   e. obtain measured phenotypic performance of the first                subset of the candidate microbial strains;            -   f. obtain a selection of a second subset of the                candidate microbial strains based at least in part upon                their measured phenotypic performance;            -   g. add to the training set of the predictive model (1)                inputs corresponding to the selected second subset of                candidate microbial strains, along with (2)                corresponding measured performance of the selected                second subset of candidate microbial strains; and            -   h. repeat (b)-(g).    -   48. The system of embodiment 47, wherein the instructions, when        executed by at least one of the one or more processors, cause        the system to repeat (b)-(g) until measured phenotypic        performance of at least one candidate microbial strain satisfies        a performance metric.    -   49. The system of embodiment 47, wherein:        -   during a first application of test inputs to the predictive            model, the genetic changes represented by the test inputs            comprise genetic changes to the one or more background            microbial strains; and        -   during subsequent applications of test inputs, the genetic            changes represented by the test inputs comprise genetic            changes to candidate microbial strains within a previously            selected second subset of candidate microbial strains.    -   50. The system of embodiment 47, wherein the selection of the        first subset of the candidate microbial strains is based at        least in part upon epistatic effects.    -   51. The system of embodiment 50, wherein the instructions, when        executed by at least one of the one or more processors, cause        the system, during a first selection of the first subset, to:        -   determine degrees of dissimilarity between performance            measures of the one or more background microbial strains in            response to application of a plurality of respective inputs            representing genetic changes to the one or more background            microbial strains; and        -   select for inclusion in the first subset at least two            candidate microbial strains based at least in part upon the            degrees of dissimilarity in the performance measures of the            one or more background microbial strains in response to            application of genetic changes incorporated into the at            least two candidate microbial strains.    -   52. The system of embodiment 51, wherein the instructions, when        executed by at least one of the one or more processors, cause        the system, during subsequent selections of the first subset,        to:        -   determine degrees of dissimilarity between performance            measures of previous first subset candidate microbial            strains in response to application of a plurality of            respective inputs representing genetic changes, wherein the            previous first subset candidate microbial strains are            strains that were selected during a previous selection of            the first subset; and        -   select for inclusion into the first subset at least two            candidate microbial strains based at least in part upon the            degrees of dissimilarity in the performance measures of the            previous first subset candidate microbial strains in            response to application of the genetic changes incorporated            into the at least two candidate microbial strains.    -   53. One or more non-transitory computer readable media storing        instructions for iteratively improving the design of candidate        microbial strains, wherein the instructions, when executed by        one or more computing devices, cause at least one of the one or        more computing devices to:        -   a. access a predictive model populated with a training set            comprising (1) inputs representing genetic changes to one or            more background microbial strains and (2) corresponding            performance measures;        -   b. apply test inputs to the predictive model that represent            genetic changes, the test inputs corresponding to candidate            microbial strains incorporating those genetic changes;        -   c. predict phenotypic performance of the candidate microbial            strains based at least in part upon the predictive model;        -   d. select a first subset of the candidate microbial strains            based at least in part upon their predicted performance;        -   e. obtain measured phenotypic performance of the first            subset of the candidate microbial strains;        -   f. obtain a selection of a second subset of the candidate            microbial strains based at least in part upon their measured            phenotypic performance;        -   g. add to the training set of the predictive model (1)            inputs corresponding to the selected second subset of            candidate microbial strains, along with (2) corresponding            measured performance of the selected second subset of            candidate microbial strains; and        -   h. repeat (b)-(g).    -   54. The computer readable media of embodiment 53, wherein the        instructions, when executed, cause at least one of the one or        more computing devices to repeat (b)-(g) until measured        phenotypic performance of at least one candidate microbial        strain satisfies a performance metric.    -   55. The computer readable media of embodiment 53, wherein:        -   during a first application of test inputs to the predictive            model, the genetic changes represented by the test inputs            comprise genetic changes to the one or more background            microbial strains; and        -   during subsequent applications of test inputs, the genetic            changes represented by the test inputs comprise genetic            changes to candidate microbial strains within a previously            selected second subset of candidate microbial strains.    -   56. The computer readable media of embodiment 53, wherein the        selection of the first subset of the candidate microbial strains        is based at least in part upon epistatic effects.    -   57. The computer readable media of embodiment 56, wherein the        instructions, when executed, cause at least one of the one or        more computing devices, during a first selection of the first        subset, to:        -   determine degrees of dissimilarity between performance            measures of the one or more background microbial strains in            response to application of a plurality of respective inputs            representing genetic changes to the one or more background            microbial strains; and        -   select for inclusion in the first subset at least two            candidate microbial strains based at least in part upon the            degrees of dissimilarity in the performance measures of the            one or more background microbial strains in response to            application of genetic changes incorporated into the at            least two candidate microbial strains.    -   58. The computer readable media of embodiment 53, wherein the        instructions, when executed, cause at least one of the one or        more computing devices, during subsequent selections of the        first subset, to:        -   determine degrees of dissimilarity between performance            measures of previous first subset candidate microbial            strains in response to application of a plurality of            respective inputs representing genetic changes, wherein the            previous first subset candidate microbial strains are            strains that were selected during a previous selection of            the first subset; and        -   select for inclusion into the first subset at least two            candidate microbial strains based at least in part upon the            degrees of dissimilarity in the performance measures of the            previous first subset candidate microbial strains in            response to application of the genetic changes incorporated            into the at least two candidate microbial strains.    -   59. A computer-implemented method for applying epistatic effects        in the iterative improvement of candidate microbial strains, the        method comprising:        -   obtaining data representing measured performance in response            to corresponding genetic changes made to at least one            microbial background strain;        -   obtaining a selection of at least two genetic changes based            at least in part upon a degree of dissimilarity between the            corresponding responsive performance measures of the at            least two genetic changes,        -   wherein the degree of dissimilarity relates to the degree to            which the at least two genetic changes affect their            corresponding responsive performance measures through            different biological pathways; and        -   designing genetic changes to a microbial background strain            that include the selected genetic changes.    -   60. The method of embodiment 59, wherein the microbial        background strain for which the at least two selected genetic        changes are designed is the same as the at least one microbial        background strain for which data representing measured        responsive performance was obtained.    -   61. A system for applying epistatic effects in the iterative        improvement of candidate microbial strains, the system        comprising:        -   one or more processors; and        -   one or more memories operatively coupled to at least one of            the one or more processors and having instructions stored            thereon that, when executed by at least one of the one or            more processors, cause the system to:        -   obtain data representing measured performance in response to            corresponding genetic changes made to at least one microbial            background strain;        -   obtain a selection of at least two genetic changes based at            least in part upon a degree of dissimilarity between the            corresponding responsive performance measures of the at            least two genetic changes,        -   wherein the degree of dissimilarity relates to the degree to            which the at least two genetic changes affect their            corresponding responsive performance measures through            different biological pathways; and        -   design genetic changes to a microbial background strain that            include the selected genetic changes.    -   62. The system of embodiment 61, wherein the microbial        background strain for which the at least two selected genetic        changes are designed is the same as the at least one microbial        background strain for which data representing measured        responsive performance was obtained.    -   63. One or more non-transitory computer readable media storing        instructions for applying epistatic effects in the iterative        improvement of candidate microbial strains, wherein the        instructions, when executed by one or more computing devices,        cause at least one of the one or more computing devices to:    -   obtain data representing measured performance in response to        corresponding genetic changes made to at least one microbial        background strain;    -   obtain a selection of at least two genetic changes based at        least in part upon a degree of dissimilarity between the        corresponding responsive performance measures of the at least        two genetic changes,    -   wherein the degree of dissimilarity relates to the degree to        which the at least two genetic changes affect their        corresponding responsive performance measures through different        biological pathways; and    -   design genetic changes to a microbial background strain that        include the selected genetic changes.    -   64. The computer readable media of embodiment 63, wherein the        microbial background strain for which the at least two selected        genetic changes are designed is the same as the at least one        microbial background strain for which data representing measured        responsive performance was obtained.

INCORPORATION BY REFERENCE

All references, articles, publications, patents, patent publications,and patent applications cited herein are incorporated by reference intheir entireties for all purposes. However, mention of any reference,article, publication, patent, patent publication, and patent applicationcited herein is not, and should not be taken as an acknowledgment or anyform of suggestion that they constitute valid prior art or form part ofthe common general knowledge in any country in the world.

What is claimed is:
 1. A computer-implemented method for engineering acandidate host cell to have a beneficial combination of geneticalterations; said method comprising the steps of: a) populating apredictive machine learning model with a training data set, containing:i) a plurality of genetic alteration input variables, representing aplurality of genetic alterations that have been introduced into a hostcell, and ii) a plurality of measured phenotypic performance outputvariables, representing phenotypic performance measurements associatedwith the plurality of introduced genetic alterations; b) generating, insilico, a pool of design candidate host cells incorporating theplurality of genetic alterations; and c) utilizing the predictivemachine learning model to predict the expected phenotypic performance ofmembers of the pool of design candidate host cells that comprise acombination of genetic alterations selected from step (a) that areuncharacterized for improving phenotypic performance at the time ofcarrying out step (c); wherein the predicted expected phenotypicperformance is production of a product of interest, said product ofinterest selected from the group consisting of: a small molecule,enzyme, protein, peptide, amino acid, organic acid, synthetic compound,fuel, alcohol, primary extracellular metabolite, secondary extracellularmetabolite, intracellular component molecule, and combinations thereof.2. The method of claim 1, wherein the predictive machine learning modelincorporates at least one of the following: linear regression, kernelridge regression, logistic regression, neural networks, support vectormachines (SVMs), decision trees, hidden Markov models, Bayesiannetworks, a Gram-Schmidt process, reinforcement-based learning,cluster-based learning, hierarchical clustering, genetic algorithms, orcombinations thereof.
 3. The method of claim 1, wherein the predictivemachine learning model incorporates epistatic effects.
 4. The method ofclaim 1, wherein the predictive machine learning model is supervised,semi-supervised, or unsupervised.
 5. The method of claim 1, wherein theplurality of genetic alterations comprise a genetic alteration selectedfrom the group consisting of: a single nucleotide polymorphism,nucleotide sequence insertion, nucleotide sequence deletion, andnucleotide sequence replacements.
 6. The method of claim 1, wherein theplurality of genetic alterations comprise a genetic alterationcomprising one or more heterologous promoters from a promoter ladderoperably linked to an endogenous target gene.
 7. The method of claim 1,comprising step d) manufacturing a member of the pool of designcandidate host cells from a previous step to create an engineered hostcell.
 8. The method of claim 7, comprising step e) measuring, in an invitro assay, phenotypic performance of the engineered host cell from theprevious step; and f) adding to the training data set of (a) i. one ormore genetic alteration input variables representing one or more geneticalterations that were introduced into the engineered host cell that wasmeasured in the previous step, and ii. one or more measured phenotypicperformance output variables representing the phenotypic performancemeasurements of the engineered host cell that was measured in theprevious step.
 9. The method of claim 8, wherein steps (a)-(f) arerepeated until an engineered host cell exhibits a desired level ofimproved phenotypic performance, provided that on the last repetition,step (f) need not be performed.
 10. A computer-implemented method forengineering a candidate host cell to have a beneficial combination ofgenetic alterations, said method comprising the steps of: a) populatinga predictive machine learning model with a training data set,containing: i) a plurality of genetic alteration input variablesrepresenting a plurality of genetic alterations that have beenintroduced into a host cell, and ii) a plurality of measured phenotypicperformance output variables representing phenotypic performancemeasurements associated with the plurality of introduced geneticalterations; b) generating, in silico, a pool of design candidate hostcells incorporating the plurality of genetic alterations; and c)utilizing the predictive machine learning model to predict the expectedphenotypic performance of members of the pool of design candidate hostcells that comprise a combination of genetic alterations selected fromstep (a) that are uncharacterized for improving phenotypic performanceat the time of carrying out step (c); wherein the predictive machinelearning model incorporates at least one of the following: linearregression, kernel ridge regression, logistic regression, neuralnetworks, support vector machines (SVMs), decision trees, hidden Markovmodels, Bayesian networks, a Gram-Schmidt process, reinforcement-basedlearning, cluster-based learning, hierarchical clustering, geneticalgorithms, or combinations thereof.
 11. The method of claim 10, whereinthe predictive machine learning model incorporates epistatic effects.12. The method of claim 10, wherein the predictive machine learningmodel is supervised, semi-supervised, or unsupervised.
 13. The method ofclaim 10, wherein the plurality of genetic alterations comprise agenetic alteration selected from the group consisting of: a singlenucleotide polymorphism, nucleotide sequence insertion, nucleotidesequence deletion, and nucleotide sequence replacements.
 14. The methodof claim 10, wherein the plurality of genetic alterations comprise agenetic alteration comprising one or more heterologous promoters from apromoter ladder operably linked to an endogenous target gene.
 15. Themethod of claim 10, comprising step d) manufacturing a member of thepool of design candidate host cells from a previous step to create anengineered host cell.
 16. The method of claim 15, comprising step e)measuring, in an in vitro assay, phenotypic performance of theengineered host cell from the previous step; and f) adding to thetraining data set of (a) i. one or more genetic alteration inputvariables representing one or more genetic alterations that wereintroduced into the engineered host cell that was measured in theprevious step, and ii. one or more measured phenotypic performanceoutput variables representing the phenotypic performance measurements ofthe engineered host cell that was measured in the previous step.
 17. Themethod of claim 16, wherein steps (a)-(f) are repeated until anengineered host cell exhibits a desired level of improved phenotypicperformance, provided that on the last repetition, step (f) need not beperformed.
 18. A computer-implemented method for engineering a candidatehost cell to have a beneficial combination of genetic alterations, saidmethod comprising the steps of: a) populating a predictive machinelearning model with a training data set, containing: i) a plurality ofgenetic alteration input variables representing a plurality of geneticalterations that have been introduced into a host cell, and ii) aplurality of measured phenotypic performance output variablesrepresenting phenotypic performance measurements associated with theplurality of introduced genetic alterations; b) generating, in silico, apool of design candidate host cells incorporating the plurality ofgenetic alterations; and c) utilizing the predictive machine learningmodel to predict the expected phenotypic performance of members of thepool of design candidate host cells that comprise a combination ofgenetic alterations selected from step (a) that are uncharacterized forimproving phenotypic performance at the time of carrying out step (c);wherein the at least one genetic alteration comprises one or moreheterologous promoters from a promoter ladder operably linked to anendogenous target gene.
 19. The method of claim 18, wherein thepredictive machine learning model incorporates epistatic effects. 20.The method of claim 18, wherein the predictive machine learning model issupervised, semi-supervised, or unsupervised.
 21. The method of claim18, wherein the plurality of genetic alterations comprise a geneticalteration selected from the group consisting of: a single nucleotidepolymorphism, nucleotide sequence insertion, nucleotide sequencedeletion, and nucleotide sequence replacements.
 22. The method of claim18, comprising step d) manufacturing a member of the pool of designcandidate host cells from a previous step to create an engineered hostcell.
 23. The method of claim 22, comprising step e) measuring, in an invitro assay, phenotypic performance of the engineered host cell from theprevious step; and f) adding to the training data set of (a) i. one ormore genetic alteration input variables representing one or more geneticalterations that were introduced into the engineered host cell that wasmeasured in the previous step, and ii. one or more measured phenotypicperformance output variables representing the phenotypic performancemeasurements of the engineered host cell that was measured in theprevious step.
 24. The method of claim 23, wherein steps (a)-(f) arerepeated until an engineered host cell exhibits a desired level ofimproved phenotypic performance, provided that on the last repetition,step (f) need not be performed.
 25. A computer-implemented method forengineering a host cell with a beneficial combination of geneticalterations, said method comprising: a) populating a predictive machinelearning model with a training data set, containing: i) a plurality ofgenetic alteration input variables representing a plurality of geneticalterations that have been introduced into a host cell, and ii) aplurality of experimentally validated phenotypic performance outputvariables representing a plurality of phenotypic performancemeasurements associated with the plurality of introduced geneticalterations; b) generating, in silica, a pool of design candidate hostcells incorporating the plurality of genetic alterations; c) utilizingthe predictive machine learning model to predict the expected phenotypicperformance of members of the pool of design candidate host cells thatcomprise a combination of genetic alterations selected from step (a)that are uncharacterized for improving phenotypic performance at thetime of carrying out step (c); and d) manufacturing a member of the poolof design candidate host cells of step (c), thereby engineering a hostcell with a beneficial combination of genetic alterations; wherein thepredicted expected phenotypic performance is production of a product ofinterest, said product of interest selected from the group consistingof: a small molecule, enzyme, protein, peptide, amino acid, organicacid, synthetic compound, fuel, alcohol, primary extracellularmetabolite, secondary extracellular metabolite, intracellular componentmolecule, and combinations thereof.
 26. The method of claim 25, whereinthe predictive machine learning model incorporates at least one of thefollowing: linear regression, kernel ridge regression, logisticregression, neural networks, support vector machines (SVMs), decisiontrees, hidden Markov models, Bayesian networks, a Gram-Schmidt process,reinforcement-based learning, cluster-based learning, hierarchicalclustering, genetic algorithms, or combinations thereof.
 27. The methodof claim 25, wherein the predictive machine learning model incorporatesepistatic effects.
 28. The method of claim 25, wherein the predictive man learning model is supervised, semi-supervised, or unsupervised. 29.The method of claim 25, comprising step e) measuring, in an in vitroassay, phenotypic performance of the engineered host cell from aprevious step; and f) adding to the training data set of (a) i. one ormore genetic alteration input variables representing one or more geneticalterations that were introduced into the engineered host cell that wasmeasured in the previous step, and ii. one or more measured phenotypicperformance output variables representing the phenotypic performancemeasurements of the engineered host cell that was measured in theprevious step.
 30. The method of claim 29, wherein steps (a)-(f) arerepeated until an engineered host cell exhibits a desired level ofimproved phenotypic performance, provided that, on the last repetition,step (f) need not be performed.